{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine tuning classification with OpenAI\n",
    "\n",
    "We will fine-tune some OpenAI classifier to classify the number of stars a reviewer will give a review of an app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd  # Used for data manipulation and analysis\n",
    "from openai import OpenAI  # OpenAI's API for accessing its AI models\n",
    "import os  # Provides functions to interact with the operating system\n",
    "from datasets import load_dataset  # Library to load and preprocess datasets\n",
    "import random  # Provides functions for generating random numbers\n",
    "import math  # Provides mathematical functions\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import seaborn as sns  # Seaborn provides a higher-level interface for styling plots\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "import math\n",
    "\n",
    "# Setting the style to a modern look\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Defining a constant SEED for reproducibility in random operations\n",
    "SEED = 42\n",
    "\n",
    "# Setting the seed for the random library to ensure consistent results\n",
    "random.seed(SEED)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI(\n",
    "    # This is the default and can be omitted\n",
    "    api_key=os.environ.get(\"OPENAI_API_KEY\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset parquet (/Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n",
      "Loading cached processed dataset at /Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-c7d4d3a6b1c12ac8.arrow\n",
      "Loading cached processed dataset at /Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-6f7d6dd258fd58db.arrow\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['package_name', 'review', 'date', 'star'],\n",
       "    num_rows: 288065\n",
       "})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'star' is a column in our dataset and we want to convert it to a ClassLabel column\n",
    "# so we can stratify our samples.\n",
    "\n",
    "# Importing the ClassLabel module to represent categorical class labels\n",
    "from datasets import ClassLabel\n",
    "\n",
    "# Loading the 'app_reviews' dataset's training split into the 'dataset' variable\n",
    "dataset = load_dataset('app_reviews', split='train')\n",
    "\n",
    "# Converting the 'star' column in our dataset to a ClassLabel type\n",
    "# This allows for categorical representation and easier handling of classes\n",
    "dataset = dataset.class_encode_column('star')\n",
    "\n",
    "# Displaying the dataset to see the changes\n",
    "dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached split indices for dataset at /Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-db7553cfcc7860a4.arrow and /Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-2960ea48d9a425ee.arrow\n",
      "Loading cached split indices for dataset at /Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-aa36e58e1017a1b1.arrow and /Users/sinanozdemir/.cache/huggingface/datasets/parquet/app_reviews-444711a5c0608be8/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7/cache-0d55c33aae5c7107.arrow\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['package_name', 'review', 'date', 'star'],\n",
       "        num_rows: 172839\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['package_name', 'review', 'date', 'star'],\n",
       "        num_rows: 57613\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['package_name', 'review', 'date', 'star'],\n",
       "        num_rows: 57613\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into a training set and a test set.\n",
    "# We reserve 20% of the data for testing and use stratification on the 'star' column\n",
    "# to ensure both sets have an equal distribution of each star category.\n",
    "dataset = dataset.train_test_split(test_size=0.2, seed=SEED, stratify_by_column='star')\n",
    "\n",
    "# Now, we further split our training dataset to reserve 25% of it for validation.\n",
    "# Again, we stratify by the 'star' column to keep the distribution consistent.\n",
    "df = dataset['train'].train_test_split(test_size=.25, seed=SEED, stratify_by_column='star')\n",
    "\n",
    "# Assigning the split datasets to their respective keys:\n",
    "# - The remaining 75% of our initial training data becomes the new training dataset.\n",
    "dataset['train'] = df['train']\n",
    "\n",
    "# - The 25% split from our initial training data becomes the validation dataset.\n",
    "dataset['val'] = df['test']\n",
    "\n",
    "# Displaying the dataset to see the distribution across train, test, and validation sets.\n",
    "dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package_name</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>org.ppsspp.ppsspp</td>\n",
       "      <td>Nice😉</td>\n",
       "      <td>March 09 2017</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Google play service Just one ward its amazing ...</td>\n",
       "      <td>December 18 2016</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Mr Perfect</td>\n",
       "      <td>April 30 2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>org.torproject.android</td>\n",
       "      <td>Does not work with Tmobile S4 If you try to in...</td>\n",
       "      <td>September 08 2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Ok</td>\n",
       "      <td>April 29 2017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             package_name                                             review  \\\n",
       "0       org.ppsspp.ppsspp                                              Nice😉   \n",
       "1  com.google.android.gms  Google play service Just one ward its amazing ...   \n",
       "2  com.google.android.gms                                         Mr Perfect   \n",
       "3  org.torproject.android  Does not work with Tmobile S4 If you try to in...   \n",
       "4  com.google.android.gms                                                 Ok   \n",
       "\n",
       "                date  star  \n",
       "0      March 09 2017     4  \n",
       "1   December 18 2016     4  \n",
       "2      April 30 2017     0  \n",
       "3  September 08 2016     0  \n",
       "4      April 29 2017     2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to pandas dataframe\n",
    "training_df = pd.DataFrame(dataset['train'])\n",
    "\n",
    "training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34740</th>\n",
       "      <td>Strangely Broken Can play Sonic Adventure 2  n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4921</th>\n",
       "      <td>Good app which give a great view of the night ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164418</th>\n",
       "      <td>Needs checkpoints 3rd boss already lost my ank...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45456</th>\n",
       "      <td>Its easy to use</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80866</th>\n",
       "      <td>Loved it</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   review\n",
       "34740   Strangely Broken Can play Sonic Adventure 2  n...\n",
       "4921    Good app which give a great view of the night ...\n",
       "164418  Needs checkpoints 3rd boss already lost my ank...\n",
       "45456                                     Its easy to use\n",
       "80866                                            Loved it"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df[['review']].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NHDjQ5r699LNlp0+ftk5bvXq1pBtnz27uE76+vnr22Wdt1mcYhtLS0nTu3DmbdZQtW1arV6/Wb7/9Zv285kb//v1tlm/atKlKly5ts628dut7bRiGXn31VU2dOlUPPvigTVudOnVUsWLFbL8vHTp0UK1atayvXVxc1KJFC0nK1j6VLl1awcHBNtOyeg8tFotCQkJszpI/+eST+TIYyq3fyXnVl3v16qVJkyapadOmNtMrVKighx56SGlpaUpISMiDPQCKFi5rBFCg0u8Bud2lZZ6engoMDNSuXbv06KOPKiAgQM2aNVOLFi3uePnXrXIyJHm1atUyHXHM399f+/fv18GDB/N1iPP9+/fL1dVVvr6+Gdpq1Kih++67T2fPnlVCQoLKli1rbbv1j05J1qGz0wfwuN02JWX6R1+ZMmXk6empP/74Q0ePHs31aG6Ojo4KCgpSUFCQLl++rF27dmnnzp3atGmTjh8/rlWrVikuLk5z58697Xrq1q2runXrKikpSXv27NGJEycUHR2tw4cPa/v27ZKU6X12ubkU7OZLNi9evKhvv/1WCQkJ6tatm0aNGmUz7L90I9A8/fTTkqQTJ07o2LFjOn36tI4dO6aIiIgsa8uusmXL2gR2KfP3eM+ePbJYLJk+3iAwMNDmXqXSpUvrX//6l7766ivrvU3NmjVT8+bN5evrm+0fNbKSWb8sXbq0Ll++rNTUVFkslrtaf2Zufa8dHBysASg2NlZ//fWXoqOjdeLECe3du9ca5rNTz918ziSpZs2aNmFLunE8bl4+OTlZhw4dUtWqVTMdzj8wMDBPn+smZfxOzqu+3KxZM0k3HkFx6NAhRUdH69SpU9q/f7/1eyezUWyB4o5wBqBApf9CfKeQ8+mnn2rhwoUKDw/X9u3btX37dk2bNk3333+/RowYocceeyxb27v1l9/bqVy5cqbT0/9oyc7Znbtx5coV3XvvvVn+Uezu7q6zZ88qMTHRJpy5uLhkmDf9DItxh0Ed0n8tT/8jMbNtSrIZGORulC5dWq1atVKrVq00fPhwfffddxo+fLg2bdqk/fv33zYAJicnKywsTEuXLrUOxFGqVCn5+PioTp061j8cb5WTPpDu1iHbhwwZot69e2v58uUqUaKEzaAH6TZt2qQPPvhAhw8fliRZLBbVrl1b9erV09GjR7M1wEZWsvseJyQkqFy5chlCgKRMf3iYOHGiGjRooDVr1igyMlK7d+9WWFiY3N3d9cYbb6hjx475XnNeyuy9PnbsmCZPnqxff/3Vut1q1aopMDBQR44c0T///JOtem63P9mRneMRHx8vSapUqVKm68iP4erTv5Nr1qxpnZYXffnChQt6//33tWHDButgRpUqVVLDhg3l7u6u06dP51s/AAozwhmAApU+EEhgYOBt53Nzc9PAgQM1cOBAxcTEaPv27dq4caN+/PFHDRo0SN9//32e3xyfVfg6f/68JFkfKnu7PzDvJsSUKlVK8fHxSk5OznBmRroxyIIkm2B2t9JHHTx//rzNJVt5sc2NGzfqP//5j7p06aLBgwdnOk/79u21detWrVixQsePH79tOJsyZYoWL16spk2bqm/fvqpbt671j9WpU6dmGc7yQvny5fXRRx+pU6dOWrhwoR588EF17drV2r53714NHDhQpUuX1vjx4+Xv769atWrJ2dlZx44dy9GluHejZMmS+t///ifDMDIEh8xGqXRyctK///1v/fvf/9bFixe1fft2bdq0Sd9++62GDx+u6tWr3/GzamZXr15Vnz599Pfff2vAgAF6/PHH9eCDD1r7ffrZHbNI/yHo1lFA093tSKO3Sk5O1u7du+Xk5GQ9Y58XfdkwDPXv318HDx5Ujx499Mwzz6hOnTrW79CuXbvm6+WtQGHGPWcACkxycrK+/PJLScpw/8vNIiMj9f777ysyMlKSdN9996ljx44KCwtTUFCQrl+/rt27d+d5fceOHcswvHNqaqoiIyPl4OBgDQ7p92hk9odS+vDZN8vur+ve3t5KS0vLNGScP39eJ06cUI0aNXJ1Juh225QyHz0zKSlJUVFRKlGiRK6CcKVKlXTu3Dl9++23t718Kf343Hfffbdd37p161SiRAl98skneuyxx2zOIhw9elRS/p2RkW6cWUh/LMLEiRNt7kFcv369UlNT9e6776pr16566KGHrAG7IGpLV79+fSUmJmb66IL0z1O6Y8eOadq0afrll18k3QigTz31lEJDQ/Xyyy9Lknbt2pXvNeenrVu36sKFC+rSpYtef/11NWjQwBrMLl68aL13yixncEqVKqUHHnhAJ0+etJ5Fu9mt7+Hd+uqrr5SQkKDHHnvM+uzCvOjLhw8f1sGDB9W8eXONHTtWAQEB1mB2/fp1nThxIlvrAYojwhmAApGUlKSQkBCdPHlSzzzzjDUUZObKlStauHChZs2aZfNHvWEYOnPmjCTbyyLvuece6/O07kZiYmKGoZ3nz5+vEydOqFWrVtbLHmvXri1J2rJli829JpGRkfr9998zrDf9ErM71di5c2dJ0vvvv29zw31iYqLGjBmjtLQ0derUKRd7dudthoWF2YSNlJQUTZgwQZcuXVL79u1thqfPLl9fXwUEBOjYsWMKCQnJdBCYP/74Q19//bUefPBBBQQEWKenB+Cbj5mLi4uSkpIyDEYQHh6uTZs2ZZg/P3Tp0kXNmzdXYmKiRo8ebZ2efnxufdbauXPn9MEHH0hShufU5VW/vdlzzz0n6caZxJvP4h46dEgrVqywmdfR0VHz5s3T9OnTM/wokd3Lj80uq/clKSlJ7777rvX75db3xp6ee+45paamavLkyTZ1bdmyRT/99FOebWfXrl16//335ezsrNdff906PS/6cvolnBcuXLCZNzU1VZMmTbKekTfTcQfMgssaAeSpgwcP2jzkNikpSTExMdq6dav+/vtvNWnSROPGjbvtOh555BG1bNlSmzZt0jPPPKNHHnlEFotF27dv18GDB/Xkk0/Kz8/POn+VKlV06tQphYSEqEmTJrm+T+a+++7T0qVLtW/fPjVo0ED79+/Xtm3bVLVqVY0dO9Y6n5eXl/WBt507d9ajjz6qmJgY/fDDD2rYsGGGs1DpN/Z//fXXcnBw0LPPPpvpM9M6dOig3377TevWrdMzzzyjli1bWp9zFh0drWbNmql///652res+Pv7a+DAgZo9e7Y6duyo1q1bW59z9tdff6lu3brWs0W58eGHH+qFF17QunXr9Msvv6hZs2aqXr26rl+/rv379+uPP/5QhQoVNGvWLJt77dKP2bhx49S4cWMNHjxYnTt31pw5c9SlSxe1a9dO99xzj/bs2aNdu3apYsWKiouLK5DR39577z0988wz2rp1q9asWaOgoCA9/fTTWrhwoWbMmKH9+/erZs2aOnv2rH7++Wc5OTnpnnvuyVBbXvXbm7Vr105t27bVDz/8oGeffVaPPvqoEhIS9OOPP6pEiRKSZD3ODzzwgHr06KEvv/xS7du3V6tWreTq6qqoqCjt2rVL/v7+1udeFVYBAQG6//779dtvv6lnz57y9/fXpUuXtHnzZsXGxqpcuXKKj49XQkJCnp6Rvhu9evXSDz/8oLVr1+rgwYNq0qSJ9bls9957ry5evJijwVoWLVpkHbgkNTVVV65c0YEDBxQRESEXFxdNmzZNDz30kHX+vOjLzz77rBo2bKg///xTXbp00cMPP6zr16/rt99+04kTJ1ShQgX9/fffjNYIZIIzZwDy1KFDhxQWFmb977PPPtP27dvl7e2tyZMna+HChdY/ErPi6OioGTNm6K233pLFYtHatWu1bNkyOTg4KCQkRNOmTbOZf/To0apVq5bWr1+vtWvX5rr2mjVrauHChbp+/bo+//xz/fXXX+ratatWrlyZ4Ub82bNnq1u3brp48aIWL16s48ePa8KECZk+XDcwMFC9e/dWUlKSlixZoqioqCxreP/99zVx4kRVr15d33zzjdatW6eyZctq9OjR+vTTT+9qaPOsvPrqq5o9e7bq16+vn3/+2XqG5Y033tCKFSuyHCwkOypVqqS1a9dq7Nix8vHx0a5du/TZZ59pxYoVunz5sgYOHKjvvvtOderUsVku/WHCu3fv1ueff64rV65oyJAhGjZsmMqUKaOVK1fq66+/tl5+lf6IgfQzaPmpevXqeu211yTJ+hBjT09PLVy4UIGBgdq+fbuWLFmigwcP6l//+pfWr1+vwMBARUdH69ixY9b15FW/vdUHH3yg119/XQ4ODlq2bJkiIiLUv39/DRgwQJJsPn+jRo3Se++9pwoVKuibb77RkiVLFB8fryFDhmjBggX50t8KkpubmxYsWKAOHTooOjpaixcv1tatW+Xj46MvvvjCOrR9+qWdZuDs7Kz58+erX79+unTpkpYuXarDhw9r1KhR1svB7/QderPFixdbv4/nzJmj1atX6/Lly+rVq5fCw8MzBPC86MsODg766KOP1KNHD126dElLlizRTz/9pBo1amjevHkaPny4JHMdd8AsHAwu+AUAoEiIiYlRiRIlrGdKbjZ9+nTNmTNHM2bMUPv27e1QHbLj9OnTKleuXKaPG3nzzTcVHh6ulStXZvq4BACFH2fOAAAoIhYsWKBGjRppw4YNNtPj4uK0du1a3XPPPdYHdsOcJk2apIYNG2rfvn02048fP66ff/5Z5cqVU926de1UHYD8xpkzAACKiL/++kvPPfec0tLS9Pjjj6tmzZr6+++/tXHjRiUkJCgkJER9+vSxd5m4jS1btuill16Sq6ur2rZtq8qVK+vcuXPauHGjkpKS9MEHH6hdu3b2LhNAPiGcAQBQhBw9elSffvqpdu7cqdjYWJUqVUre3t4KDg7O9sPbYV+RkZFasGCB9uzZo7i4OJUtW1b+/v7q27ev/P397V0egHxEOAMAAAAAE+CeMwAAAAAwAcIZAAAAAJgAD6HOJ2lpaUpJSZGjo6McHBzsXQ4AAAAAOzEMQ2lpaXJycrrtg+QJZ/kkJSVFe/futXcZAAAAAEzCx8dHzs7OWbYTzvJJeiL28fGRxWKxczWFQ2pqqvbu3csxQ76jr6Gg0NdQUOhrKCj0tdxJP263O2smEc7yTfqljBaLhY6bQxwzFBT6GgoKfQ0Fhb6GgkJfy5073e7EgCAAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAgGxzc3OzdwlFFuEMAAAAMCEjLc3eJWRgsVjk7e0ti8Vi71IyZcZjlhNO9i4AAAAAQEYOjo5KWDpbKRfO2ruUQsGpclWVfX6gvcu4K4QzAAAAwKRSLpxVypkT9i4DBYTLGgEAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHThLPk5GR16NBBO3bssE6Ljo5Wnz595Ofnp6eeekq///67zTJbt25Vhw4d5Ovrq+DgYEVHR9u0f/bZZ2rRooX8/f01YsQIJSYmWtuSkpI0YsQIBQYGqnnz5lqwYIHNsnfaNgAAAADkJVOEs6SkJL3xxhs6cuSIdZphGBo0aJAqVqyo1atX69lnn9XgwYN19uxZSdLZs2c1aNAgBQUFadWqVSpfvrwGDhwowzAkSRs2bFBYWJjGjRunRYsWKSoqSlOmTLGuPzQ0VPv27dOiRYs0ZswYhYWF6fvvv8/WtgEAAAAgr9k9nB09elRdu3bVqVOnbKZv375d0dHRGjdunGrXrq0BAwbIz89Pq1evliStXLlS9evXV9++ffXQQw9p0qRJOnPmjHbu3ClJWrx4sXr37q1WrVqpQYMGeu+997R69WolJibq6tWrWrlypUaOHKl69erpiSee0IsvvqgvvvgiW9sGAAAAgLxm93C2c+dONWnSRMuXL7eZHhUVJW9vb5UoUcI6LSAgQJGRkdb2wMBAa5ubm5vq1aunyMhIpaamau/evTbtfn5+un79ug4dOqRDhw4pJSVF/v7+NuuOiopSWlraHbcNAAAAAHnNyd4FPP/885lOj42NVeXKlW2mVahQQTExMXdsv3TpkpKSkmzanZycVLZsWcXExMjR0VHlypWTs7Oztb1ixYpKSkpSQkLCHbedE6mpqTleprhKP1YcM+Q3+hoKCn0NBYW+VjRZLBZ7l1AomfFzkN2a7B7OspKYmGgTniTJ2dlZycnJd2y/du2a9XVm7YZhZNom3RiY5E7bzom9e/fmeJnijmOGgkJfQ0Ghr6Gg0NeKDjc3N3l7e9u7jELp8OHDNgMBFiamDWcuLi5KSEiwmZacnCxXV1dr+61hKTk5WWXKlJGLi4v19a3tbm5uSk1NzbRNklxdXe+47Zzw8fHhV49sSr8clWOG/EZfQ0Ghr6Gg0NeA/+Pp6WnvEjJI/4zeiWnDmbu7u44ePWozLS4uznq5obu7u+Li4jK0e3l5qWzZsnJxcVFcXJxq164tSUpJSVFCQoIqVaokwzAUHx+vlJQUOTndOASxsbFydXVVmTJl7rjtnLBYLHxJ5hDHDAWFvoaCQl9DQaGvAYX7clC7DwiSFV9fX+3fv996iaIkRUREyNfX19oeERFhbUtMTNSBAwfk6+srR0dH+fj42LRHRkbKyclJdevWlZeXl5ycnGwG+IiIiJCPj48cHR3vuG0AAAAAyGumDWeNGzdWlSpVFBISoiNHjmjevHnas2ePunTpIknq3Lmz/vzzT82bN09HjhxRSEiIqlevriZNmki6MdDI/PnztXHjRu3Zs0djx45V165d5ebmJjc3N3Xs2FFjx47Vnj17tHHjRi1YsEDBwcHZ2jYAAAAA5DXThjOLxaLZs2crNjZWQUFB+vrrr/XRRx+patWqkqTq1atr1qxZWr16tbp06aKEhAR99NFHcnBwkCQ9/fTTGjBggEaPHq2+ffuqQYMGGjZsmHX9ISEhqlevnnr37q333ntPQ4YMUdu2bbO1bQAAAADIaw6GYRj2LqIoSk1NVWRkpPz8/Ar1da8FiWOGgkJfQ0Ghr6Gg0NeKrrgZo5Ry5oS9yygUnKrdr4qv/cfeZWQqu59R0545AwAAAIDihHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmIDpw9m5c+c0YMAANWzYUK1bt9Znn31mbTtw4ICee+45+fr6qnPnztq3b5/NsuHh4WrTpo18fX01aNAgXbx40dpmGIamTp2qpk2bqnHjxgoNDVVaWpq1PT4+XkOGDJG/v79at26tr776Kt/3FQAAAEDxZfpw9tprr6lEiRJas2aNRowYoRkzZujHH3/U1atX1b9/fwUGBmrNmjXy9/fXgAEDdPXqVUnSnj17NHLkSA0ePFjLly/XpUuXFBISYl3vwoULFR4errCwMM2cOVPr16/XwoULre0hISG6fPmyli9frldeeUWjRo3Snj17Cnz/AQAAABQPTvYu4Hb++ecfRUZGavz48br//vt1//33q0WLFtq2bZv++ecfubi46O2335aDg4NGjhypX3/9Vd9//72CgoK0ZMkStW/fXh07dpQkhYaGqlWrVoqOjlaNGjW0ePFiDR06VIGBgZKkt956Sx9++KH69eunU6dO6ZdfftFPP/2k6tWry8PDQ5GRkVq6dKkaNGhgxyMCAAAAoKgy9ZkzV1dXubm5ac2aNbp+/bqOHz+uP//8U15eXoqKilJAQIAcHBwkSQ4ODmrYsKEiIyMlSVFRUdbgJUlVqlRR1apVFRUVpfPnz+vcuXNq1KiRtT0gIEBnzpzRhQsXFBUVpSpVqqh69eo27bt37y6YHQcAAABQ7Jj6zJmLi4tGjx6t8ePHa/HixUpNTVVQUJCee+45/fTTT6pTp47N/BUqVNCRI0ckSRcuXFDlypUztMfExCg2NlaSbNorVqwoSdb2zJY9f/58jvchNTU1x8sUV+nHimOG/EZfQ0Ghr6Gg0NeKJovFYu8SCiUzfg6yW5Opw5kkHTt2TK1atdILL7ygI0eOaPz48Xr44YeVmJgoZ2dnm3mdnZ2VnJwsSbp27VqW7deuXbO+vrlNkpKTk++47pzYu3dvjpcp7jhmKCj0NRQU+hoKCn2t6HBzc5O3t7e9yyiUDh8+rMTERHuXkSumDmfbtm3TqlWrtHnzZrm6usrHx0fnz5/Xxx9/rBo1amQIS8nJyXJ1dZV046xbZu1ubm42QczFxcX6/9KND0JWy6avOyd8fHz41SObUlNTtXfvXo4Z8h19DQWFvoaCQl8D/o+np6e9S8gg/TN6J6YOZ/v27VOtWrVsQpG3t7fmzJmjwMBAxcXF2cwfFxdnvRzR3d090/ZKlSrJ3d1dkhQbG2u9ryz9Usf09qyWzSmLxcKXZA5xzFBQ6GsoKPQ1FBT6GlC4Lwc19YAglStX1smTJ23OYh0/flzVq1eXr6+vdu/eLcMwJN14btmff/4pX19fSZKvr68iIiKsy507d07nzp2Tr6+v3N3dVbVqVZv2iIgIVa1aVZUrV5afn5/OnDmjmJgYm3Y/P7983mMAAAAAxZWpw1nr1q11zz33aNSoUfrvf/+rn3/+WXPmzFGvXr3Url07Xbp0SRMmTNDRo0c1YcIEJSYmqn379pKkHj166KuvvtLKlSt16NAhvf3222rZsqVq1KhhbZ86dap27NihHTt2aNq0aQoODpYk1ahRQ82bN9ewYcN06NAhrVy5UuHh4erZs6fdjgUAAACAos3UlzWWLl1an332mSZMmKAuXbqofPnyeuWVV9StWzc5ODho7ty5GjNmjFasWCFPT0/NmzdPJUqUkCT5+/tr3Lhxmjlzpv755x81a9ZM48ePt667X79++vvvvzV48GBZLBZ16dJFffr0sbaHhoZq5MiR6tq1qypVqqSJEyfyjDMAAAAA+cbU4UyS6tSpo4ULF2ba1qBBA61duzbLZYOCghQUFJRpm8ViUUhIiEJCQjJtr1ChgubMmZPzggEAAAAgF0x9WSMAAAAAFBeEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKmD2fJycl677331KhRIz3yyCP64IMPZBiGJOnAgQN67rnn5Ovrq86dO2vfvn02y4aHh6tNmzby9fXVoEGDdPHiRWubYRiaOnWqmjZtqsaNGys0NFRpaWnW9vj4eA0ZMkT+/v5q3bq1vvrqq4LZYQAAAADFkunD2X/+8x9t3bpV8+fP17Rp07RixQotX75cV69eVf/+/RUYGKg1a9bI399fAwYM0NWrVyVJe/bs0ciRIzV48GAtX75cly5dUkhIiHW9CxcuVHh4uMLCwjRz5kytX79eCxcutLaHhITo8uXLWr58uV555RWNGjVKe/bsKfD9BwAAAFA8ONm7gNtJSEjQ6tWrtXDhQjVo0ECS1LdvX0VFRcnJyUkuLi56++235eDgoJEjR+rXX3/V999/r6CgIC1ZskTt27dXx44dJUmhoaFq1aqVoqOjVaNGDS1evFhDhw5VYGCgJOmtt97Shx9+qH79+unUqVP65Zdf9NNPP6l69ery8PBQZGSkli5daq0DAAAAAPKSqcNZRESESpUqpcaNG1un9e/fX5L07rvvKiAgQA4ODpIkBwcHNWzYUJGRkQoKClJUVJReeukl63JVqlRR1apVFRUVJWdnZ507d06NGjWytgcEBOjMmTO6cOGCoqKiVKVKFVWvXt2mfe7cuTneh9TU1BwvU1ylHyuOGfIbfQ0Fhb6GgkJfK5osFou9SyiUzPg5yG5Npg5n0dHRqlatmtatW6c5c+bo+vXrCgoK0iuvvKLY2FjVqVPHZv4KFSroyJEjkqQLFy6ocuXKGdpjYmIUGxsrSTbtFStWlCRre2bLnj9/Psf7sHfv3hwvU9xxzFBQ6GsoKPQ1FBT6WtHh5uYmb29ve5dRKB0+fFiJiYn2LiNXTB3Orl69qpMnT2rZsmWaNGmSYmNjNXr0aLm5uSkxMVHOzs428zs7Oys5OVmSdO3atSzbr127Zn19c5t0YwCSO607J3x8fPjVI5tSU1O1d+9ejhnyHX0NBYW+hoJCXwP+j6enp71LyCD9M3on+RLOYmJidN999931epycnHTlyhVNmzZN1apVkySdPXtWX375pWrVqpUhLCUnJ8vV1VWS5OLikmm7m5ubTRBzcXGx/r9041eKrJZNX3dOWCwWviRziGOGgkJfQ0Ghr6Gg0NeAwn05aK5Ga/Ty8spy5MJdu3apffv2d1VUukqVKsnFxcUazCTpgQce0Llz5+Tu7q64uDib+ePi4qyXI2bVXqlSJbm7u0uS9fLGm/8/vT2rZQEAAAAgP2T7zNmCBQusw9QbhqGVK1fq119/zTDf7t27M1wSmFu+vr5KSkrSf//7Xz3wwAOSpOPHj6tatWry9fXVJ598IsMw5ODgIMMw9Oeff+rll1+2LhsREaGgoCBJ0rlz53Tu3Dn5+vrK3d1dVatWVUREhHXQj4iICFWtWlWVK1eWn5+fzpw5Y3MGMCIiQn5+fnmyXwAAAABwq2yHs6SkJIWFhUm6MTLiypUrM8zj6Oio0qVL65VXXsmT4h588EG1bNlSISEhGjt2rGJjYzVv3jy98sorateunaZNm6YJEyaoe/fuWrZsmRITE61n7Xr06KFevXrJz89PPj4+mjBhglq2bKkaNWpY26dOnWoNX9OmTVPfvn0lSTVq1FDz5s01bNgwjRw5Unv37lV4eLiWLFmSJ/sFAAAAALfKdjh75ZVXrKGrbt26WrFiRYE882vq1KkaP368evToITc3N/Xs2VO9evWSg4OD5s6dqzFjxmjFihXy9PTUvHnzVKJECUmSv7+/xo0bp5kzZ+qff/5Rs2bNNH78eOt6+/Xrp7///luDBw+WxWJRly5d1KdPH2t7aGioRo4cqa5du6pSpUqaOHEizzgDAAAAkG8cDMMw7F1EUZSamqrIyEj5+fkV6psSCxLHDAWFvoaCQl9DQaGvFV1xM0Yp5cwJe5dRKDhVu18VX/uPvcvIVHY/o7kerXHLli365ZdflJiYqLS0NJs2BwcHTZw4MberBgAAAIBiJ1fhbMGCBQoNDZWLi4vKly8vBwcHm/ZbXwMAAAAAbi9X4WzJkiV65plnNGHChDwbmREAAAAAirNcPecsLi5OXbp0IZgBAAAAQB7JVTjz9vbWkSNH8roWAAAAACi2cnVZ44gRI/Taa6+pRIkS8vX1lZubW4Z5qlatetfFAQAAAEBxkatw1qNHD6WlpWnEiBFZDv5x8ODBuyoMAAAAAIqTXIWz8ePHMyIjAAAAAOShXIWzoKCgvK4DAAAAAIq1XIWzP/74447zNGrUKDerBgAAAIBiKVfhrFevXnJwcJBhGNZpt17myD1nAAAAAJB9uQpnixcvzjDt6tWr2rVrl7766ivNmjXrrgsDAAAAgOIkV+GscePGmU5v2bKlSpQooY8//lhz5869q8IAAAAAoDjJ1UOobycwMFA7d+7M69UCAAAAQJGW5+Hs559/VsmSJfN6tQAAAABQpOXqssbg4OAM09LS0hQTE6MzZ87opZdeuuvCAAAAAKA4yVU4u3mUxnSOjo7y8PDQgAED1Llz57suDAAAAACKk1yFs88//zyv6wAAAACAYi1X4Szdr7/+qp07d+rSpUsqX768AgIC1KJFi7yqDQAAAACKjVyFs+TkZA0cOFC///67LBaLypUrp/j4eM2dO1dNmzbV3Llz5ezsnNe1AgAAAECRlavRGmfNmqWIiAiFhoZqz549+v333xUVFaVJkyYpMjJSH3/8cV7XCQAAAABFWq7CWXh4uAYPHqx//etfslgskiQnJyd17NhRgwcP1vr16/O0SAAAAAAo6nIVzi5evChvb+9M27y9vXX+/Pm7KgoAAAAAiptchbOaNWsqIiIi07Y//vhDVapUuauiAAAAAKC4ydWAIN27d9f7778vV1dXPf3006pYsaLi4uIUHh6uTz75RIMHD87rOgEAAACgSMtVOOvRo4cOHDigqVOnatq0adbphmGoU6dO6t+/f54VCAAAAADFQa6H0p8wYYL69u2rnTt36p9//pGDg4PatGmj2rVr53WNAAAAAFDk5eies8OHD6tz585auHChJKl27drq0aOHnn/+eX344Yd644039N///jdfCgUAAACAoizb4ez06dMKDg5WXFycHnjgAZu2e+65R2+//bYSEhL0/PPPM1ojAAAAAORQtsPZvHnzVLZsWa1du1bt2rWzaXNzc1OfPn20atUqubi4aO7cuXleKAAAAAAUZdkOZ9u2bdOLL76o8uXLZzlPpUqV1LdvX23ZsiVPigMAAACA4iLb4ezChQu6//777zifh4eHYmJi7qYmAAAAACh2sh3OypcvrwsXLtxxvvj4eN177713VRQAAAAAFDfZDmeNGjXSmjVr7jjfunXr5O3tfVdFAQAAAEBxk+1w1qtXL+3YsUPvv/++kpKSMrQnJycrNDRUv/76q3r27JmnRQIAAABAUZfth1D7+PgoJCREEydO1FdffaWHH35Y1atXV2pqqs6ePasdO3YoPj5er776qlq0aJGfNQMAAABAkZPtcCZJPXv2VN26dTV//nz99NNP1jNoJUuWVPPmzdW3b1/5+vrmS6EAAAAAUJTlKJxJUkBAgAICAiRJFy9elJOTk8qUKZPnhQEAAABAcZLjcHaz2z3zDAAAAACQfdkeEAQAAAAAkH8IZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQKVTjr37+/3nnnHevrAwcO6LnnnpOvr686d+6sffv22cwfHh6uNm3ayNfXV4MGDdLFixetbYZhaOrUqWratKkaN26s0NBQpaWlWdvj4+M1ZMgQ+fv7q3Xr1vrqq6/yfwcBAAAAFFuFJpx988032rx5s/X11atX1b9/fwUGBmrNmjXy9/fXgAEDdPXqVUnSnj17NHLkSA0ePFjLly/XpUuXFBISYl1+4cKFCg8PV1hYmGbOnKn169dr4cKF1vaQkBBdvnxZy5cv1yuvvKJRo0Zpz549BbfDAAAAAIqVQhHOEhISFBoaKh8fH+u0b7/9Vi4uLnr77bdVu3ZtjRw5UiVLltT3338vSVqyZInat2+vjh07qm7dugoNDdXmzZsVHR0tSVq8eLGGDh2qwMBANW3aVG+99Za++OILSdKpU6f0yy+/6D//+Y88PDz03HPP6V//+peWLl1a8DsPAAAAoFhwsncB2TF58mQ9++yzunDhgnVaVFSUAgIC5ODgIElycHBQw4YNFRkZqaCgIEVFRemll16yzl+lShVVrVpVUVFRcnZ21rlz59SoUSNre0BAgM6cOaMLFy4oKipKVapUUfXq1W3a586dm+PaU1NTc7PLxVL6seKYIb/R11BQ6GsoKPS1oslisdi7hELJjJ+D7NZk+nC2bds27dq1S+vXr9fYsWOt02NjY1WnTh2beStUqKAjR45Iki5cuKDKlStnaI+JiVFsbKwk2bRXrFhRkqztmS17/vz5HNe/d+/eHC9T3HHMUFDoaygo9DUUFPpa0eHm5iZvb297l1EoHT58WImJifYuI1dMHc6SkpI0ZswYjR49Wq6urjZtiYmJcnZ2tpnm7Oys5ORkSdK1a9eybL927Zr19c1tkpScnHzHdeeEj48Pv3pkU2pqqvbu3csxQ76jr6Gg0NdQUOhrwP/x9PS0dwkZpH9G78TU4SwsLEz169dXixYtMrS5uLhkCEvJycnWEJdVu5ubm00Qc3Fxsf6/dONXijutOycsFgtfkjnEMUNBoa+hoNDXUFDoa0DhvhzU1OHsm2++UVxcnPz9/SX9X4DasGGDOnTooLi4OJv54+LirJcjuru7Z9peqVIlubu7S7pxaWT6fWXplzqmt2e1LAAAAADkB1OP1vj5559r/fr1WrdundatW6fWrVurdevWWrdunXx9fbV7924ZhiHpxnPL/vzzT/n6+kqSfH19FRERYV3XuXPndO7cOfn6+srd3V1Vq1a1aY+IiFDVqlVVuXJl+fn56cyZM4qJibFp9/PzK5gdBwAAAFDsmPrMWbVq1WxelyxZUpJUq1YtVahQQdOmTdOECRPUvXt3LVu2TImJiWrfvr0kqUePHurVq5f8/Pzk4+OjCRMmqGXLlqpRo4a1ferUqbrvvvskSdOmTVPfvn0lSTVq1FDz5s01bNgwjRw5Unv37lV4eLiWLFlSULsOAAAAoJgxdTi7nVKlSmnu3LkaM2aMVqxYIU9PT82bN08lSpSQJPn7+2vcuHGaOXOm/vnnHzVr1kzjx4+3Lt+vXz/9/fffGjx4sCwWi7p06aI+ffpY20NDQzVy5Eh17dpVlSpV0sSJE9WgQYOC3k0AAAAAxUShCmfvv/++zesGDRpo7dq1Wc4fFBSkoKCgTNssFotCQkIUEhKSaXuFChU0Z86c3BcLAAAAADlg6nvOAAAAAKC4IJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnAAAAAGAChDMAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAAAAJkA4AwAAAAATIJwBAAAAgAkQzgAAAADABAhnMBU3Nzd7lwAAAADYBeGsmEpLM+xdQgYWi0Xe3t6yWCz2LiVTZjxmAAAAKDqc7F0A7MPR0UGfb7im8/Fp9i6lUHAv56heT7rauwwAAAAUYYSzYux8fJpOxxLOAAAAADPgskYAAAAAMAHCGQAAAACYAOEMAAAAAEyAcAYAAFAE8DgaoPBjQBAAAIAcMIw0OTiY6/ft9MfRmJUZjxlgRoQzAACAHHBwcFTynk0yriTYu5RCwaFUWTk3aGnvMoBCgXAGAACQQ8aVBBmX/7Z3GQCKGM4vAwAAAIAJEM4AAAAAwARMH87Onz+voUOHqnHjxmrRooUmTZqkpKQkSVJ0dLT69OkjPz8/PfXUU/r9999tlt26das6dOggX19fBQcHKzo62qb9s88+U4sWLeTv768RI0YoMTHR2paUlKQRI0YoMDBQzZs314IFC/J/ZwEAAAAUW6YOZ4ZhaOjQoUpMTNQXX3yh6dOn65dfftGMGTNkGIYGDRqkihUravXq1Xr22Wc1ePBgnT17VpJ09uxZDRo0SEFBQVq1apXKly+vgQMHyjAMSdKGDRsUFhamcePGadGiRYqKitKUKVOs2w4NDdW+ffu0aNEijRkzRmFhYfr+++/tchwAAAAAFH2mHhDk+PHjioyM1JYtW1SxYkVJ0tChQzV58mQ9+uijio6O1rJly1SiRAnVrl1b27Zt0+rVqzVkyBCtXLlS9evXV9++fSVJkyZNUrNmzbRz5041adJEixcvVu/evdWqVStJ0nvvvad+/fpp2LBhMgxDK1eu1CeffKJ69eqpXr16OnLkiL744gu1a9fObscDAAAAQNFl6jNnlSpV0qeffmoNZumuXLmiqKgoeXt7q0SJEtbpAQEBioyMlCRFRUUpMDDQ2ubm5qZ69eopMjJSqamp2rt3r027n5+frl+/rkOHDunQoUNKSUmRv7+/zbqjoqKUlpaWT3sLAAAAoDgz9ZmzMmXKqEWLFtbXaWlpWrJkiZo2barY2FhVrlzZZv4KFSooJiZGkm7bfunSJSUlJdm0Ozk5qWzZsoqJiZGjo6PKlSsnZ2dna3vFihWVlJSkhIQElS9fPtv7kJqamqN9LigWi8XeJRRKZn0/kTPp7yPvJ/Ibfa1o4t/Q3OFzkHP0tdwxY1/Lbk2mDme3mjJlig4cOKBVq1bps88+swlPkuTs7Kzk5GRJUmJiYpbt165ds77OrN0wjEzbJFnXn1179+7N0fwFwc3NTd7e3vYuo1A6fPiwzcAxKNzM+PlE0URfKzr4NzT3+Dc0Z+hruVeY+1qhCWdTpkzRokWLNH36dHl4eMjFxUUJCQk28yQnJ8vV1VWS5OLikiFIJScnq0yZMnJxcbG+vrXdzc1NqampmbZJsq4/u3x8fPjVowjx9PS0dwnIA+mXNvP5RH6jrwH/h39DUVDM2NfS/z24k0IRzsaPH68vv/xSU6ZM0ZNPPilJcnd319GjR23mi4uLs16q6O7urri4uAztXl5eKlu2rFxcXBQXF6fatWtLklJSUpSQkKBKlSrJMAzFx8crJSVFTk43DlFsbKxcXV1VpkyZHNVusVj4B7kI4b0sWvh8oqDQ1wD+DUXBKcx9zdQDgkhSWFiYli1bpg8++EBPP/20dbqvr6/2799vvURRkiIiIuTr62ttj4iIsLYlJibqwIED8vX1laOjo3x8fGzaIyMj5eTkpLp168rLy0tOTk7WwUXS1+3j4yNHR9MfMgAAAACFkKmTxrFjxzR79my99NJLCggIUGxsrPW/xo0bq0qVKgoJCdGRI0c0b9487dmzR126dJEkde7cWX/++afmzZunI0eOKCQkRNWrV1eTJk0kSc8//7zmz5+vjRs3as+ePRo7dqy6du0qNzc3ubm5qWPHjho7dqz27NmjjRs3asGCBQoODrbn4QAAAABQhJn6ssaffvpJqamp+vjjj/Xxxx/btB0+fFizZ8/WyJEjFRQUpFq1aumjjz5S1apVJUnVq1fXrFmzNHHiRH300Ufy9/fXRx99JAcHB0nS008/rTNnzmj06NFKTk5W27ZtNWzYMOv6Q0JCNHbsWPXu3VulSpXSkCFD1LZt24LbeQAAAADFiqnDWf/+/dW/f/8s22vVqqUlS5Zk2f7YY4/psccey9X63dzcNHnyZE2ePDn7BQMAAABALpn6skYAAAAAKC4IZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADABwhkAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAA5CM3Nzd7lwAAKCSc7F0AAAB5wTAMOTg42LsMGxaLRd7e3vYuI0tmPGYAUJwRzgAARYKDg4MunD+v5ORke5dSKDg7O6uyu7u9ywAA3IRwBgAoMpKTkwlnAIBCi3vOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBqBYcnNzs3cJAAAANpzsXQCAos0wDDk4ONi7DBsWi0Xe3t72LiNLZjxmAAAg/xHOAOQrBwcH7TiSpkuJ9q6kcCjjJjV5iIsaAAAojghnAPLdpUQp4aq9qwAAADA3fp4FAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAAAAAJgA4QwAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDObiMpKUkjRoxQYGCgmjdvrgULFti7JAAAAABFlJO9CzCz0NBQ7du3T4sWLdLZs2c1fPhwVa1aVe3atbN3aQAAAACKGMJZFq5evaqVK1fqk08+Ub169VSvXj0dOXJEX3zxBeEMAAAAQJ7jssYsHDp0SCkpKfL397dOCwgIUFRUlNLS0uxYGQAAAICiiDNnWYiNjVW5cuXk7OxsnVaxYkUlJSUpISFB5cuXv+3yhmFIkpKTk2WxWPK11tywWCyqUt6QxdGwdymFQuWyhlJTU5WammrvUgodi8WiMq5pEl0tW8q4SqmpBn0tFywWi5ycnPgBLZucnJz4Xssli8WitJJlZcjB3qUUCg4l76Wv5ZLFYpHjfTXkaOFP9uxwrFTFtH0tvab0jJAV3uksJCYm2gQzSdbXycnJd1w+/Y+DAwcO5H1xeaRuhRv/IXsiI+1dQeHlJOn2P2fA6jJ9DQXnzNmz9i6hECstuZS2dxGFQ4r4YrsbDwVKD9m7iELE5H3tTj8gEs6y4OLikiGEpb92dXW94/JOTk7y8fGRo6OjHBz4ZQ0AAAAorgzDUFpampycbh+/CGdZcHd3V3x8vFJSUqwHMTY2Vq6uripTpswdl3d0dMxw5g0AAAAAssKAIFnw8vKSk5OTIm86NRoREWE9GwYAAAAAeYmUkQU3Nzd17NhRY8eO1Z49e7Rx40YtWLBAwcHB9i4NAAAAQBHkYNxpyJBiLDExUWPHjtUPP/ygUqVKqV+/furTp4+9ywIAAABQBBHOAAAAAMAEuKwRAAAAAEyAcAYAAAAAJkA4AwAAAAATIJzBFJKSkjRixAgFBgaqefPmWrBggb1LQhGXnJysDh06aMeOHfYuBUXU+fPnNXToUDVu3FgtWrTQpEmTlJSUZO+yUASdPHlS/fr1k7+/v1q2bKlPP/3U3iWhGOjfv7/eeecde5dR5PAQaphCaGio9u3bp0WLFuns2bMaPny4qlatqnbt2tm7NBRBSUlJevPNN3XkyBF7l4IiyjAMDR06VGXKlNEXX3yhf/75RyNGjJCjo6OGDx9u7/JQhKSlpal///7y8fHR2rVrdfLkSb3xxhtyd3fXM888Y+/yUER988032rx5szp16mTvUooczpzB7q5evaqVK1dq5MiRqlevnp544gm9+OKL+uKLL+xdGoqgo0ePqmvXrjp16pS9S0ERdvz4cUVGRmrSpEl66KGHFBgYqKFDhyo8PNzepaGIiYuLk5eXl8aOHav7779fjz32mB5++GFFRETYuzQUUQkJCQoNDZWPj4+9SymSCGewu0OHDiklJUX+/v7WaQEBAYqKilJaWpodK0NRtHPnTjVp0kTLly+3dykowipVqqRPP/1UFStWtJl+5coVO1WEoqpy5cqaMWOGSpUqJcMwFBERoT/++EONGze2d2kooiZPnqxnn31WderUsXcpRRKXNcLuYmNjVa5cOTk7O1unVaxYUUlJSUpISFD58uXtWB2Kmueff97eJaAYKFOmjFq0aGF9nZaWpiVLlqhp06Z2rApFXevWrXX27Fm1atVKTz75pL3LQRG0bds27dq1S+vXr9fYsWPtXU6RxJkz2F1iYqJNMJNkfZ2cnGyPkgAgT02ZMkUHDhzQ66+/bu9SUITNnDlTc+bM0cGDBzVp0iR7l4MiJikpSWPGjNHo0aPl6upq73KKLM6cwe5cXFwyhLD013z4ARR2U6ZM0aJFizR9+nR5eHjYuxwUYen3ACUlJemtt97S22+/neHHTyC3wsLCVL9+fZurApD3CGewO3d3d8XHxyslJUVOTje6ZGxsrFxdXVWmTBk7VwcAuTd+/Hh9+eWXmjJlCpeZIV/ExcUpMjJSbdq0sU6rU6eOrl+/ritXrnBrAPLMN998o7i4OOsYAek/pG/YsEG7d++2Z2lFCuEMdufl5SUnJydFRkYqMDBQkhQRESEfHx85OnLlLYDCKSwsTMuWLdMHH3zAY0GQb06fPq3Bgwdr8+bNcnd3lyTt27dP5cuXJ5ghT33++edKSUmxvp46daok6a233rJXSUUS4Qx25+bmpo4dO2rs2LGaOHGiLly4oAULFnC9PIBC69ixY5o9e7b69++vgIAAxcbGWtsqVapkx8pQ1Pj4+KhevXoaMWKEQkJCdObMGU2ZMkUvv/yyvUtDEVOtWjWb1yVLlpQk1apVyx7lFFmEM5hCSEiIxo4dq969e6tUqVIaMmSI2rZta++yACBXfvrpJ6Wmpurjjz/Wxx9/bNN2+PBhO1WFoshisWj27NkaP368unXrJjc3N/Xq1UvBwcH2Lg1ALjgYhmHYuwgAAAAAKO64oQcAAAAATIBwBgAAAAAmQDgDAAAAABMgnAEAAACACRDOAAAAAMAECGcAAAAAYAKEMwAAAAAwAcIZAKBYKayP9yysdQMAso9wBgAo9P766y+9/vrratasmerXr6/mzZvrtdde06FDh2zmi4iIUP/+/fOtjlmzZsnT0zPDf35+fnryySc1ffp0paSk5GidMTEx6t+/v86cOWOd1rp1a73zzjt5XT4AwM6c7F0AAAB348iRI+rWrZv8/Pw0atQoVahQQTExMVqyZIm6du2qxYsXy8/PT5K0cuVKHTt2LN9rWr58uc3r+Ph4hYeHa86cOUpJSdGwYcOyva6tW7dq8+bNNtPCwsJUqlSpPKkVAGAehDMAQKG2cOFClStXTp988omcnP7vn7U2bdqoXbt2mj17tubNm1egNaWHwZu1atVKp0+f1po1a3IUzjLj7e19V8sDAMyJyxoBAIVaXFycDMNQWlqazfQSJUpoxIgRat++vSTpnXfe0dq1a3XmzBl5enpqzZo1kqTTp0/r7bffVvPmzVWvXj09/PDDevvttxUfH29dV+vWrTVx4kT17t1bDRo00MiRI3NVa6lSpeTg4GB9nZqaqnnz5qlDhw5q0KCB/Pz81L17d23fvl2StGbNGoWEhEiSHn/8ceuljDdf1nj69Gl5enrqu+++09ChQ+Xv76/GjRtr1KhRunr1qnVb169f19SpU/Xoo4+qQYMG6tevn9atWydPT0+dPn06V/sDAMhbnDkDABRqLVu21ObNm9W9e3d17txZTZs21YMPPigHBwe1a9fOOt/AgQN18eJFHThwQGFhYapZs6YSExMVHByscuXKacyYMSpdurR2796tsLAwubq6aty4cdblv/jiC73wwgt66aWXVLJkydvWdPN9ZWlpaYqPj9fXX3+tLVu26IUXXrC2TZ06VV9++aXefPNNeXp66vz58/roo4/06quvatOmTWrZsqVeeeUVffzxxwoLC5Onp2eW2xwzZow6d+6s2bNna8+ePZo+fbrKlSunN998U5I0evRohYeHa8iQIfLy8lJ4eLjefffdHB9vAED+IZwBAAq1559/XrGxsZo/f741TJUrV07NmzdXcHCwGjRoIEmqWbOmypcvL2dnZ+tlhwcPHtR9992nyZMnq0aNGpKkpk2bKioqSjt37rTZTtWqVfXWW29lq6Z69eplmFa1alUNGTLEZkCSCxcu6PXXX1evXr2s01xcXDRkyBAdPnxYfn5+qlmzpiTJy8tL1atXz3Kbjz32mIYPHy5Jevjhh7VlyxZt2rRJb775pk6dOqW1a9dq+PDh1nDYokULxcXF6ffff8/WPgEA8h/hDABQ6L366qvq06ePfvvtN23btk07duzQ+vXrFR4erhEjRig4ODjT5by8vLR06VKlpaXpxIkTOnnypI4eParjx49nGFXRy8sr2/WsWrVKkpSYmKjPPvtMO3bs0KhRo/T444/bzDdt2jRJ0sWLF3X8+HGdPHlSv/zyiyQpOTk529uTMt7ndt9991lHeNyxY4cMw7A5kyhJHTp0IJwBgIkQzgAARcK9996rDh06qEOHDpKkAwcOaNiwYZoyZYqeeeYZlStXLtPlFi5cqDlz5ighIUEVK1ZU/fr15ebmpsuXL9vMV6JEiWzX4uPjY/3/wMBA9enTR6+++qo+++wzBQYGWtv27t2r9957T3v37pWbm5vq1KmjqlWrSsr5c83c3NxsXjs6OlrXcfHiRUlShQoVbOa59TUAwL4YEAQAUGidP39ezZs318qVKzO0eXt76/XXX1dycrKio6MzXX79+vV6//339dJLL2nbtm3asmWL5s6dq/vvvz/PanR0dNSkSZPk5OSkd955R0lJSZKkK1eu6MUXX1SJEiX0zTff6M8//9SqVavUuXPnPNt2Ond3d0k3Bk+5WXpoAwCYA+EMAFBoVaxYUU5OTlq6dKk19Nzs+PHjcnFxUa1atSTdCEo3i4iIUJkyZfTiiy+qfPnykqT//e9/ioiIyDD6492oVq2aBg4cqOjoaH3yySfW2hISEhQcHKw6depYa/v1118lybr9W2vOjYCAAFksFv34448203/44Ye7XjcAIO9wWSMAoNCyWCwaO3asBg0apM6dO6tnz56qXbu2EhMTtWXLFn3xxRd69dVXde+990qSypQpo7i4OG3evFleXl5q0KCBvvzyS73//vtq1aqVLly4oPnz5ysuLs66TF7p06ePVq1apU8++USdOnXSAw88oFKlSmnOnDlycnKSk5OTNmzYYHO/WnrNkvTjjz/q0UcfVe3atXO87Ro1aqhz58764IMPdP36ddWtW1c//vij9f62vAiAAIC7x7cxAKBQa9mypVasWCEPDw/NmTNH/fr10xtvvKGDBw9q+vTpNqMjBgUFqVq1aho0aJDWrVunTp06adCgQfruu+/00ksvaebMmQoMDNS4ceOUkJCgY8eO5Vmdzs7OGjFihK5du6bJkyerdOnSmj17tgzD0Kuvvqq3335bZ8+e1ZIlS1SyZEnt2rVLktSkSRM98sgjmjZtmiZPnpzr7b/77rvq3r27FixYoIEDByomJkavvPKKpJzdTwcAyD8ORk7vOAYAAIVKQkKCfv31V7Vo0cJmYJTJkydrzZo12rFjhx2rAwCk47JGAACKODc3N02YMEFeXl7q3bu3SpQoocjISC1ZskQDBgywd3kAgP+PM2cAABQDBw8e1IwZMxQZGanExETVrFlT3bt3V8+ePeXg4GDv8gAAIpwBAAAAgCkwIAgAAAAAmADhDAAAAABMgHAGAAAAACZAOAMAAAAAEyCcAQAAAIAJEM4AAAAAwAQIZwAAAABgAoQzAAAAADCB/wd81nCF976woAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counting the number of occurrences for each 'star' value, sorting by index\n",
    "star_counts = training_df['star'].value_counts().sort_index()\n",
    "\n",
    "# Creating the bar plot to show distribution of star ratings given\n",
    "plt.figure(figsize=(10, 6))  # Setting a suitable figure size for better visibility\n",
    "star_counts.plot(kind='bar', color=sns.color_palette(\"coolwarm\", len(star_counts)))\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Distribution of Star Ratings in Training Data', fontsize=15)\n",
    "plt.xlabel('Star Rating', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xticks(rotation=0)  # Ensure the x-axis labels are horizontal for better readability\n",
    "\n",
    "# Displaying the chart\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.DataFrame(dataset['test'])\n",
    "val_df = pd.DataFrame(dataset['val'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Star prediction (sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Creating the 'prompt' column in each dataset (training, validation, and test) by adding a separator '###\\n' to the 'review' column.\n",
    "# This separator is often used in fine-tuning to signal where the prompt ends and the expected output begins.\n",
    "training_df['prompt'] = training_df['review'] + '\\n###\\n'\n",
    "val_df['prompt'] = val_df['review'] + '\\n###\\n'\n",
    "test_df['prompt'] = test_df['review'] + '\\n###\\n'\n",
    "\n",
    "# Converting the 'star' column in each dataset to a string format and storing it in the 'completion' column.\n",
    "# The 'completion' column will be used as the target variable for sentiment analysis.\n",
    "training_df['completion'] = training_df['star'].astype(str)  # for sentiment\n",
    "val_df['completion'] = val_df['star'].astype(str)  # for sentiment\n",
    "test_df['completion'] = test_df['star'].astype(str)  # for sentiment\n",
    "\n",
    "# Creating a training dataset in JSONL format after dropping duplicates based on the 'prompt' column.\n",
    "# Random sampling ensures the data is shuffled.\n",
    "training_df.sample(\n",
    "    len(training_df)\n",
    ").drop_duplicates(subset=['prompt'])[['prompt', 'completion']].to_json(\n",
    "    \"app-review-full-train-sentiment-random.jsonl\", orient='records', lines=True\n",
    ")\n",
    "\n",
    "# Creating another version of the training dataset in JSONL format, but this time ordering by 'completion' column (star ratings).\n",
    "# This ordered set might be useful for specific training strategies.\n",
    "training_df.sample(\n",
    "    len(training_df)\n",
    ").drop_duplicates(subset=['prompt'])[['prompt', 'completion']].sort_values('completion').to_json(\n",
    "    \"app-review-full-train-sentiment-ordered.jsonl\", orient='records', lines=True\n",
    ")\n",
    "\n",
    "# Creating a validation dataset in JSONL format after dropping duplicates based on the 'prompt' column.\n",
    "val_df.sample(\n",
    "    len(val_df)\n",
    ").drop_duplicates(subset=['prompt'])[['prompt', 'completion']].to_json(\n",
    "    \"app-review-full-val-sentiment-random.jsonl\", orient='records', lines=True\n",
    ")\n",
    "\n",
    "# Creating a test dataset in JSONL format after dropping duplicates based on the 'prompt' column.\n",
    "test_df.sample(\n",
    "    len(test_df)\n",
    ").drop_duplicates(subset=['prompt'])[['prompt', 'completion']].to_json(\n",
    "    \"app-review-full-test-sentiment-random.jsonl\", orient='records', lines=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package_name</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>star</th>\n",
       "      <th>prompt</th>\n",
       "      <th>completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>org.ppsspp.ppsspp</td>\n",
       "      <td>Nice😉</td>\n",
       "      <td>March 09 2017</td>\n",
       "      <td>4</td>\n",
       "      <td>Nice😉\\n###\\n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Google play service Just one ward its amazing ...</td>\n",
       "      <td>December 18 2016</td>\n",
       "      <td>4</td>\n",
       "      <td>Google play service Just one ward its amazing ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Mr Perfect</td>\n",
       "      <td>April 30 2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Mr Perfect\\n###\\n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>org.torproject.android</td>\n",
       "      <td>Does not work with Tmobile S4 If you try to in...</td>\n",
       "      <td>September 08 2016</td>\n",
       "      <td>0</td>\n",
       "      <td>Does not work with Tmobile S4 If you try to in...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Ok</td>\n",
       "      <td>April 29 2017</td>\n",
       "      <td>2</td>\n",
       "      <td>Ok\\n###\\n</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172834</th>\n",
       "      <td>com.nilhcem.hostseditor</td>\n",
       "      <td>9c Ok</td>\n",
       "      <td>January 15 2016</td>\n",
       "      <td>4</td>\n",
       "      <td>9c Ok\\n###\\n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172835</th>\n",
       "      <td>org.thialfihar.android.apg</td>\n",
       "      <td>GRATE Start 1st off I waited a few weeks to te...</td>\n",
       "      <td>December 12 2014</td>\n",
       "      <td>2</td>\n",
       "      <td>GRATE Start 1st off I waited a few weeks to te...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172836</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>Gtty</td>\n",
       "      <td>November 20 2016</td>\n",
       "      <td>4</td>\n",
       "      <td>Gtty\\n###\\n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172837</th>\n",
       "      <td>com.google.android.gms</td>\n",
       "      <td>OK</td>\n",
       "      <td>March 05 2017</td>\n",
       "      <td>4</td>\n",
       "      <td>OK\\n###\\n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172838</th>\n",
       "      <td>org.telegram.messenger</td>\n",
       "      <td>Good Fast</td>\n",
       "      <td>January 31 2017</td>\n",
       "      <td>4</td>\n",
       "      <td>Good Fast\\n###\\n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172839 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      package_name  \\\n",
       "0                org.ppsspp.ppsspp   \n",
       "1           com.google.android.gms   \n",
       "2           com.google.android.gms   \n",
       "3           org.torproject.android   \n",
       "4           com.google.android.gms   \n",
       "...                            ...   \n",
       "172834     com.nilhcem.hostseditor   \n",
       "172835  org.thialfihar.android.apg   \n",
       "172836      com.google.android.gms   \n",
       "172837      com.google.android.gms   \n",
       "172838      org.telegram.messenger   \n",
       "\n",
       "                                                   review               date  \\\n",
       "0                                                   Nice😉      March 09 2017   \n",
       "1       Google play service Just one ward its amazing ...   December 18 2016   \n",
       "2                                              Mr Perfect      April 30 2017   \n",
       "3       Does not work with Tmobile S4 If you try to in...  September 08 2016   \n",
       "4                                                      Ok      April 29 2017   \n",
       "...                                                   ...                ...   \n",
       "172834                                              9c Ok    January 15 2016   \n",
       "172835  GRATE Start 1st off I waited a few weeks to te...   December 12 2014   \n",
       "172836                                               Gtty   November 20 2016   \n",
       "172837                                                 OK      March 05 2017   \n",
       "172838                                          Good Fast    January 31 2017   \n",
       "\n",
       "        star                                             prompt completion  \n",
       "0          4                                       Nice😉\\n###\\n          4  \n",
       "1          4  Google play service Just one ward its amazing ...          4  \n",
       "2          0                                  Mr Perfect\\n###\\n          0  \n",
       "3          0  Does not work with Tmobile S4 If you try to in...          0  \n",
       "4          2                                          Ok\\n###\\n          2  \n",
       "...      ...                                                ...        ...  \n",
       "172834     4                                       9c Ok\\n###\\n          4  \n",
       "172835     2  GRATE Start 1st off I waited a few weeks to te...          2  \n",
       "172836     4                                        Gtty\\n###\\n          4  \n",
       "172837     4                                          OK\\n###\\n          4  \n",
       "172838     4                                   Good Fast\\n###\\n          4  \n",
       "\n",
       "[172839 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_dict(row, system=''):\n",
    "    return {\n",
    "        \"messages\": [\n",
    "            {\"role\": \"system\", \"content\": system},\n",
    "            {\"role\": \"user\", \"content\": row['review']},\n",
    "            {\"role\": \"assistant\", \"content\": str(row['star'])}\n",
    "        ]\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export 3.5 prompts with no system prompt\n",
    "with open(f'app-review-full-train-sentiment-random-3.5.jsonl', 'w') as f:\n",
    "    for index, row in training_df.iterrows():\n",
    "        json_str = json.dumps(get_data_dict(row))  # Create a dictionary for each row\n",
    "        f.write(json_str + '\\n')  # Write JSON string to a file\n",
    "\n",
    "with open(f'app-review-full-test-sentiment-random-3.5.jsonl', 'w') as f:\n",
    "    for index, row in test_df.iterrows():\n",
    "        json_str = json.dumps(get_data_dict(row))  # Create a dictionary for each row\n",
    "        f.write(json_str + '\\n')  # Write JSON string to a file\n",
    "\n",
    "with open(f'app-review-full-val-sentiment-random-3.5.jsonl', 'w') as f:\n",
    "    for index, row in val_df.iterrows():\n",
    "        json_str = json.dumps(get_data_dict(row))  # Create a dictionary for each row\n",
    "        f.write(json_str + '\\n')  # Write JSON string to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export 3.5 prompts with a system prompt\n",
    "system_prompt = 'You predict star ratings given an app review from 0-4 where 0 means the review would give the worst rating and 4 means the best rating'\n",
    "# 31 extra tokens PER example\n",
    "with open(f'app-review-full-train-sentiment-random-3.5-system.jsonl', 'w') as f:\n",
    "    for index, row in training_df.iterrows():\n",
    "        json_str = json.dumps(get_data_dict(row, system=system_prompt))  # Create a dictionary for each row\n",
    "        f.write(json_str + '\\n')  # Write JSON string to a file\n",
    "\n",
    "with open(f'app-review-full-test-sentiment-random-3.5-system.jsonl', 'w') as f:\n",
    "    for index, row in test_df.iterrows():\n",
    "        json_str = json.dumps(get_data_dict(row, system=system_prompt))  # Create a dictionary for each row\n",
    "        f.write(json_str + '\\n')  # Write JSON string to a file\n",
    "\n",
    "with open(f'app-review-full-val-sentiment-random-3.5-system.jsonl', 'w') as f:\n",
    "    for index, row in val_df.iterrows():\n",
    "        json_str = json.dumps(get_data_dict(row, system=system_prompt))  # Create a dictionary for each row\n",
    "        f.write(json_str + '\\n')  # Write JSON string to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice😉\n",
      "###\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(training_df['prompt'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df['star'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using OpenAI's Fine-tuning API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prompt': 'Its good\\n###\\n', 'completion': '3'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reminder of what this data looks like\n",
    "json.loads(open(\"app-review-full-train-sentiment-random.jsonl\", \"rb\").readlines()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a file object for the training dataset with OpenAI's API.\n",
    "# The 'file' parameter specifies the path to the training data in JSONL format.\n",
    "# The 'purpose' is set to 'fine-tune', indicating the file's intended use.\n",
    "training_file = client.files.create(\n",
    "  file=open(\"app-review-full-train-sentiment-random.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")\n",
    "\n",
    "# Creating a file object for the validation dataset with OpenAI's API.\n",
    "val_file = client.files.create(\n",
    "  file=open(\"app-review-full-val-sentiment-random.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Initiating the fine-tuning process with OpenAI's API.\n",
    "\n",
    "# The `client.fine_tuning.jobs.create method is used to start the training.\n",
    "# Parameters include:\n",
    "# - `training_file`: The ID of the previously uploaded training dataset file.\n",
    "# - `validation_file`: The ID of the previously uploaded validation dataset file.\n",
    "# - `model`: The base model to be fine-tuned. In this case, \"babbage-002\" is chosen.\n",
    "# - `hyperparameters`: Dictionary containing training hyperparameters. Here, we specify the number of epochs as 1.\n",
    "job = client.fine_tuning.jobs.create(\n",
    "    training_file=training_file.id,\n",
    "    validation_file=val_file.id,\n",
    "    model=\"babbage-002\",\n",
    "    hyperparameters={'n_epochs': 1}\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Retrieving details of an ongoing or completed fine-tuning job using OpenAI's API.\n",
    "\n",
    "# The `client.fine_tuning.retrieve.retrieve` method fetches the job details based on its ID.\n",
    "# This can be used to monitor the progress, status, or retrieve results of the training job.\n",
    "job = client.fine_tuning.jobs.retrieve(job.id)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FineTuningJob(id='ftjob-H8qk5ylqK4WC5CZqZbdHoI34', created_at=1698416338, error=None, fine_tuned_model='ft:babbage-002:personal::8EJokS4B', finished_at=1698423460, hyperparameters=Hyperparameters(n_epochs=1, batch_size=90, learning_rate_multiplier=2), model='babbage-002', object='fine_tuning.job', organization_id='org-azQm6jrJ3FSBpJAwYcgxQxGu', result_files=['file-X7wJXtZmKpF04r0Egc0X0m3l'], status='succeeded', trained_tokens=2833557, training_file='file-OynKOPOBvIesnfq0Ubsu3p9n', validation_file='file-yF5vsOt3IMOhJs2jFeK46nWo')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ftjob-H8qk5ylqK4WC5CZqZbdHoI34'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FineTuningJobEvent(id='ftevent-zTLF7f5eKffMUUTD7qWhbPKu', created_at=1698423465, level='info', message='The job has successfully completed', object='fine_tuning.job.event', data={}, type='message')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-hqFVXm5UFD8kbo0tvxyqdGPj', created_at=1698423463, level='info', message='New fine-tuned model created: ft:babbage-002:personal::8EJokS4B', object='fine_tuning.job.event', data={}, type='message')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-JKmXVtxPBR3I80nH6l6gVlTJ', created_at=1698423455, level='info', message='Step 1501/1501: training loss=1.07, validation loss=0.74', object='fine_tuning.job.event', data={'step': 1501, 'train_loss': 1.0739907026290894, 'valid_loss': 0.7359817861061957, 'train_mean_token_accuracy': 0.5681818127632141, 'valid_mean_token_accuracy': 0.7555555555555555}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-eBgKJTvQAsqU1PrKUAO2WftU', created_at=1698423426, level='info', message='Step 1401/1501: training loss=0.73, validation loss=0.66', object='fine_tuning.job.event', data={'step': 1401, 'train_loss': 0.7323288917541504, 'valid_loss': 0.6627644852528142, 'train_mean_token_accuracy': 0.7555555701255798, 'valid_mean_token_accuracy': 0.7555555555555555}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-yIpuUGNEvbM1DH7qiv5c32a1', created_at=1698423400, level='info', message='Step 1301/1501: training loss=0.77, validation loss=0.88', object='fine_tuning.job.event', data={'step': 1301, 'train_loss': 0.7653228044509888, 'valid_loss': 0.8825400600261573, 'train_mean_token_accuracy': 0.7222222089767456, 'valid_mean_token_accuracy': 0.7}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-97eIhKyp0i81amOiIIPGWgaW', created_at=1698423369, level='info', message='Step 1201/1501: training loss=0.77, validation loss=0.70', object='fine_tuning.job.event', data={'step': 1201, 'train_loss': 0.7747395634651184, 'valid_loss': 0.6982891914641691, 'train_mean_token_accuracy': 0.7444444298744202, 'valid_mean_token_accuracy': 0.7444444444444445}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-RtOk5ouzKkNHd8wxfXUyvBpt', created_at=1698423343, level='info', message='Step 1101/1501: training loss=0.73, validation loss=0.76', object='fine_tuning.job.event', data={'step': 1101, 'train_loss': 0.7285148501396179, 'valid_loss': 0.7625727774678833, 'train_mean_token_accuracy': 0.7666666507720947, 'valid_mean_token_accuracy': 0.7333333333333333}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-4BQgReZHYL05WkNC68dd2xfP', created_at=1698423315, level='info', message='Step 1001/1501: training loss=0.61, validation loss=0.89', object='fine_tuning.job.event', data={'step': 1001, 'train_loss': 0.6138778924942017, 'valid_loss': 0.8910756412893533, 'train_mean_token_accuracy': 0.7888888716697693, 'valid_mean_token_accuracy': 0.7}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-JeLjhPLYaMOr2AoaE6y9EJxK', created_at=1698423287, level='info', message='Step 901/1501: training loss=0.90, validation loss=0.72', object='fine_tuning.job.event', data={'step': 901, 'train_loss': 0.9008299708366394, 'valid_loss': 0.715636325089468, 'train_mean_token_accuracy': 0.7333333492279053, 'valid_mean_token_accuracy': 0.7888888888888889}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-qMCRV17wJpG0zjmC79l9KtK0', created_at=1698423258, level='info', message='Step 801/1501: training loss=0.89, validation loss=0.88', object='fine_tuning.job.event', data={'step': 801, 'train_loss': 0.8941911458969116, 'valid_loss': 0.8845321447071102, 'train_mean_token_accuracy': 0.6777777671813965, 'valid_mean_token_accuracy': 0.6555555555555556}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-AigRgkmpRidYnOgrj7aJg8oa', created_at=1698423232, level='info', message='Step 701/1501: training loss=0.80, validation loss=0.91', object='fine_tuning.job.event', data={'step': 701, 'train_loss': 0.8000747561454773, 'valid_loss': 0.912503844499588, 'train_mean_token_accuracy': 0.7555555701255798, 'valid_mean_token_accuracy': 0.6444444444444445}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-eN6NXdVbvmtsMuGSoXJdigGb', created_at=1698423204, level='info', message='Step 601/1501: training loss=1.03, validation loss=0.71', object='fine_tuning.job.event', data={'step': 601, 'train_loss': 1.0317691564559937, 'valid_loss': 0.7089030509607659, 'train_mean_token_accuracy': 0.6000000238418579, 'valid_mean_token_accuracy': 0.7333333333333333}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-EtTELoRSaLbPTLgaNcjI837P', created_at=1698423176, level='info', message='Step 501/1501: training loss=0.79, validation loss=0.90', object='fine_tuning.job.event', data={'step': 501, 'train_loss': 0.7926527261734009, 'valid_loss': 0.9036278430372476, 'train_mean_token_accuracy': 0.7111111283302307, 'valid_mean_token_accuracy': 0.7}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-9su3x1cpiMGfGejVqhAD0cJy', created_at=1698423147, level='info', message='Step 401/1501: training loss=1.05, validation loss=0.87', object='fine_tuning.job.event', data={'step': 401, 'train_loss': 1.054675817489624, 'valid_loss': 0.8725522753058208, 'train_mean_token_accuracy': 0.6222222447395325, 'valid_mean_token_accuracy': 0.6666666666666666}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-gXUQUyZrZi4GL5ttIXliifrN', created_at=1698423119, level='info', message='Step 301/1501: training loss=0.71, validation loss=0.76', object='fine_tuning.job.event', data={'step': 301, 'train_loss': 0.7131847739219666, 'valid_loss': 0.7573238644438486, 'train_mean_token_accuracy': 0.7444444298744202, 'valid_mean_token_accuracy': 0.7444444444444445}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-HiWAEadUxiKLhu0vw5wjtVzo', created_at=1698423093, level='info', message='Step 201/1501: training loss=0.95, validation loss=0.79', object='fine_tuning.job.event', data={'step': 201, 'train_loss': 0.9459320306777954, 'valid_loss': 0.7868474796828296, 'train_mean_token_accuracy': 0.6777777671813965, 'valid_mean_token_accuracy': 0.7111111111111111}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-NyA3WMGr3XiEAKyGfUEWCqj3', created_at=1698423064, level='info', message='Step 101/1501: training loss=0.85, validation loss=0.96', object='fine_tuning.job.event', data={'step': 101, 'train_loss': 0.8466601967811584, 'valid_loss': 0.9611257559102443, 'train_mean_token_accuracy': 0.7111111283302307, 'valid_mean_token_accuracy': 0.7}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-eToQAHGvQn2m2UCufXcCjXx5', created_at=1698423036, level='info', message='Step 1/1501: training loss=2.37, validation loss=2.10', object='fine_tuning.job.event', data={'step': 1, 'train_loss': 2.3732564449310303, 'valid_loss': 2.096224159002304, 'train_mean_token_accuracy': 0.3777777850627899, 'valid_mean_token_accuracy': 0.4777777777777778}, type='metrics')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-gJZvyp73lD8J8fKyenPqu0f2', created_at=1698422742, level='info', message='Fine-tuning job started', object='fine_tuning.job.event', data=None, type='message')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-rX4PTvBFuZcWXsQFOgO3aurB', created_at=1698422742, level='info', message='Files validated, moving job to queued state', object='fine_tuning.job.event', data={}, type='message')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-qMGFrt6Zib0KIiFDhrZfpZpk', created_at=1698416338, level='info', message='Validating training file: file-OynKOPOBvIesnfq0Ubsu3p9n and validation file: file-yF5vsOt3IMOhJs2jFeK46nWo', object='fine_tuning.job.event', data={}, type='message')\n",
      "---\n",
      "FineTuningJobEvent(id='ftevent-dYyy1kQvWIrDM4PRN0nYynt7', created_at=1698416338, level='info', message='Created fine-tuning job: ftjob-H8qk5ylqK4WC5CZqZbdHoI34', object='fine_tuning.job.event', data={}, type='message')\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for event in client.fine_tuning.jobs.list_events(fine_tuning_job_id=\"ftjob-H8qk5ylqK4WC5CZqZbdHoI34\"):\n",
    "    print(event)\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['file-X7wJXtZmKpF04r0Egc0X0m3l']\n"
     ]
    }
   ],
   "source": [
    "if len(job.result_files):\n",
    "    print(job.result_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y9/9xqbqkg90tnc0cmm0dxt985m0000gn/T/ipykernel_44017/1863415972.py:5: DeprecationWarning: The `.content()` method should be used instead\n",
      "  results = client.files.retrieve_content(job.result_files[0])\n"
     ]
    }
   ],
   "source": [
    "# Downloading the results of the completed fine-tuning job using OpenAI's API.\n",
    "\n",
    "# The `client.files.retrieve_content` method is used to fetch the result files.\n",
    "# `job.result_files[0]` gets the ID of the first result file associated with the job.\n",
    "results = client.files.retrieve_content(job.result_files[0])\n",
    "\n",
    "# Saving the decoded results into a CSV file.\n",
    "# Opening (or creating) a file named 'results.csv' in write mode.\n",
    "with open('results.csv', 'w') as f:\n",
    "    f.write(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_mean_token_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.37326</td>\n",
       "      <td>0.37778</td>\n",
       "      <td>2.09622</td>\n",
       "      <td>0.47778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.51663</td>\n",
       "      <td>0.37778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.24874</td>\n",
       "      <td>0.38889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.39119</td>\n",
       "      <td>0.41111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.41051</td>\n",
       "      <td>0.41111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  train_loss  train_accuracy  valid_loss  valid_mean_token_accuracy\n",
       "0     1     2.37326         0.37778     2.09622                    0.47778\n",
       "1     2     2.51663         0.37778         NaN                        NaN\n",
       "2     3     2.24874         0.38889         NaN                        NaN\n",
       "3     4     2.39119         0.41111         NaN                        NaN\n",
       "4     5     2.41051         0.41111         NaN                        NaN"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.read_csv('results.csv')\n",
    "\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_loss' values from the 'results_df' DataFrame.\n",
    "# We drop any NaN values to ensure a smooth plot.\n",
    "plt.figure(figsize=(10, 6))  # Setting a suitable figure size for better visibility\n",
    "results_df['valid_loss'].dropna().plot(color=\"royalblue\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Loss Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Validation Loss', fontsize=12)\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABCt9DRw4EFtbW7Zs2aIf1tasWTMmTZoUr/cGwMLCgvLlywPw4sWLBPfp379/vEn81apVw9raOtmv80N0w+N++OGHeD0SWbJkYeLEiRgZGbF+/foEzwsODqZnz574+fkxc+ZMmjZtGu/x9evXo9VqGTlyZLzrajQaxowZg5GRERs3bkxw3U6dOpEzZ07917a2tvrfBV9f34++Hl1vRcuWLfXHXFxcKFGiBI8ePUqw1LXud2H48OHkzp1bfzx79ux8//33DB06lLdv3/L8+XNOnjyJo6Mj3bp1i3eNzp0707dvXypUqPDRfB/y3zZUFIVhw4Yxffp0ihcvHu+xkiVLkitXrni/+8eOHePly5e0bdtW/3MFcW0+YsQIvvrqK/2Klm3atAGIN0RTURS2bdtGrly5cHV1/WBWXQ+stbV1kl5bjhw5APSrZtauXRs7Ozv27NkTr6dy586d+l40gNjYWDZu3IitrS0jRoyI93ufJ08e+vTpQ1RUlP77+K4mTZokKVtK6tevn/7/m5qaUq5cOQC6dOmClZWV/rFKlSoB8f9mrVu3DiMjI3788cd4v/fW1tZ8/fXXKIqi/30VQvyPrPInRCZlZWVFo0aN2LJlC2fPntUXKjt37iQqKireUulFixalaNGiREdHc+PGDR4+fMiTJ0+4ffs2p0+fBuLedCSHbqihi4tLgscqVarEihUr4h2rWbMmAC9fvsTHxwdfX18eP37M9evXuX79OkCiczk+5OHDh4SHh1OrVi0sLCwSPK4bonPjxo14x21sbBIMRdIVhVFRUcnKAHFvug4fPszevXv1b5R37txJvnz5qFSpUqJte/XqVf3/JrZfl6WlJcHBwTx8+JBSpUpRsWJFKlasSFhYGLdu3eLx48c8fvyYmzdv6veASqz9/vsmGuLeXL1+/ZrY2FiMjY2T/Xr/6/r161hYWOjf+L2rUKFC5M2bl6dPn/Ly5UtsbGz0jw0bNoyAgABy5MhBlSpVEjxX10anTp1K8D0EyJo1Kw8fPuTNmzdkzZpVfzyx16z7/v533tV/3blzh6tXr1KiRAmcnZ3jPdaqVStmzJjB2rVr9T/P8L/fhcSKITc3N9zc3AD0+ykldp6ZmVmShyR+yH9X/dRoNPoPCwIDA7l9+za+vr48fPiQq1ev6otw3c+Crp0Ty1iyZEn9hxQQN1QvZ86c7Nmzh++//x4zMzP9Yh69e/eO9wFHYnRLoEdERCTptenmg+kKKxMTE7788kuWLVvGiRMn9EMHt23bhqWlpX5e1oMHDwgLCyNv3rzMnz8/wXX9/PwA9H+H3s2X1su0W1tbx/swAOI+mAAoXLhwvOO6v3mRkZEAhIeHc/fuXbJkyZLokN5Xr14BCV+nEEIKKiEytTZt2rBlyxa2b9+uL6i2bNmCqalpvE/XFUVh+fLlLF26VL8cuaWlJaVLl8bJyYnAwMBkb5yp+8f53U9MdRJ7E6JbUGHv3r36T5Pt7OyoWLEiefLk4cmTJ8nO8Pr1a+D9n3DnyZMHSPiGzdzcPMG5uk+tk5sB4t40m5ubs3v3brp168arV684ceIE3bp1S7BqmY6u/RKbg/Ku0NBQIO7T/GnTprFlyxb9wgA2NjaUL1+ewoUL4+Pjk2j2lH6tiQkLCyN79uzv3aMsT548PH36lIiIiHgFVXBwMPXq1ePw4cP88ssvzJw5M97zdG30sU11X716Fa+g+pzXrPv0/t69e+/d7+zw4cMEBATof750Pasf62lJ6nmfw9LSMsGxe/fuMWXKlHgb5BYoUIDKlStz584dQkND9ceTk9HU1JQWLVqwfPlyjhw5QsOGDfW9Vbreqw/RFQj379/XF50fcufOHSB+0di6dWuWLVvG9u3bqVu3Lvfu3ePGjRu0aNFC/7dJ9zvk7+//wTlEuvN0EvuQJrXpiqfEJPZz/S7d38Pw8PBkvU4hhBRUQmRqVapU0U9anjBhAk+ePOHKlSs0btw4Xg/MypUrmTJlCk5OTkyaNAlHR0fy58+vXzXr2LFjyb637o3xq1evEmxq+9/V1BRFoX///ty8eZNOnTrx5ZdfUrJkSX3h1b59+08agqZ7wxQQEJDo47o35O++iU8NVlZW1K5dm4MHDxIQEMCJEyeIjo6mefPm732OrgDYsGFDoj07//Xtt99y8OBBGjduTMeOHbG3t9d/kj1ixAh8fHxS5sV8AisrK0JCQoiKisLMzCzB47o3cP/9Pvz+++80atQIDw8Pdu7cSbNmzeK9sda10cmTJ9+7cXJKio6O1i9m0K5du0SL4QsXLnD37l02btyoXyxF9yb49evXCXoXoqKi0Gg0mJqaxjsvMeHh4fpzPlQAJrVHR3fNnj178uLFCwYMGICbmxvFixfX/+6829P239fysYwQVzgtX76cHTt2ULduXfbt24eLiwslS5b8aLYGDRqwcOFC9u3bF2+YW2KCgoK4ePEiOXLkiLc4hL29PWXKlOHQoUNEREToP6Bo3bq1/hzdz1Ht2rXx9PT8aK70Svc6ixcvzu7du1VOI0T6InOohMjENBoNbdq04fXr1xw/fly/hO5/957SfWq8ePFi3N3dKViwoL434e7du590b91wqHdXmNLx9vaO9/WtW7e4efMmtWrVYuLEiVSqVElfTEVHR/Pw4UMg/pvH9/XsvKt48eJYWlpy69atRD911Q2Fs7e3T9qL+gxNmzZFURQOHDjA7t27KVq0KGXKlHnv+U5OTgBcuXIl0cdnzpzJokWLCA8P59WrVxw6dIiiRYsyZ84cqlevHu+Nu+57mFI9TslVunRptFotFy5cSPBYQEAADx8+pFChQgl6T5ydnTE1NeXnn3/GyMhIv2KkzofaKDIykt9++43ly5en2Os+fPgwwcHBVKlShZ9//pmffvopwX8jRowA4nqydEM5dStSJpZzw4YNuLi4sHnzZv15//39gLjvnZubGw0aNADQz39JbPn8R48eJfk1nTp1iufPn9O2bVtGjBiBi4uLvpgKDg7Wz5/SteGHMt6+fZsKFSowbtw4/bFSpUrh4uLC8ePHOXz4MOHh4fGKmQ8pW7YslStXxtvbO8FS+f81bdo0oqKiaN++fYKNfT08PAgPD+fEiRPs2LGDfPnyxZurWbx4cSwsLPDx8Ul0SO+5c+eYPn16grlx6Y2VlRWFChXC19c30Tmxd+7cYcqUKe/dN0+IzEwKKiEyOd3yxfv372ffvn0UKFCAGjVqxDtHN3Tl6dOn8Y6fOXNGv2DDx+aW/FebNm0wMjJi7ty58fZgun//Pn///Xe8c3VDVZ4/fx5v8nhsbCyTJ0/WF0PvPqabf/GhXLohR2/evOG3336L9/wnT57o9/BJ6hu8z1GvXj0sLS3x8vLi9OnTH9x7CuIWPDA1NeXPP//k/v378R5bs2YNixYtYu/evWTJkgVTU1OMjIx49epVvKXUIa5I1u0jlpT9nlKDbnjX77//Hu+NXEREBBMmTECr1X7we1C+fHk6d+5MYGAgkydP1h/XzQOcOnVqguXEZ8+ezcqVK7l06VKSiu+k2LRpE/Dhn5e6detiZ2fHs2fPOHz4MIB+8YN58+bpF0yAuF6eVatWYWRkRPXq1SlYsCBVqlTh+vXrCRYG+PvvvwkODtb/7pYoUQKI6517twi4fPkyJ06cSPJret/vfmRkJOPHj9fPu9P97DRo0AArKys2btwYb0sGRVH0y7/XqlUr3rXatGmj35zX3Nz8gz2z//X777+TNWtWfvjhBzZu3JigOA4PD2fChAls2bIFR0fHRLdQaN68OaampixYsIAnT57QsmXLeMNPdZuhBwYGMm3atHhzDUNCQhg/fjxLlizRz0VKz9q2bUt0dDQTJ06M93Pz9u1bJkyYwLJly/TDvoUQ/yND/oTI5PLkyUPt2rXZs2cPERERDB06NMFcFg8PDy5dukS/fv1o0qQJ2bJlw8fHh1OnTpEjRw6CgoKSvcKek5MTQ4YMYe7cubRq1Qo3Nzeio6PZu3cvuXLline9okWLUrFiRS5evEjbtm2pXr060dHRHD9+nIcPH5IzZ05evHgR7zn58uUDYM6cOZw/f57BgwcnmmP06NFcunSJLVu2cP36dapVq6bv0Xn9+jVDhgxJdMGDlJYlSxbq1Kmj//T3YwVVwYIFmTBhAj/++COtWrXC3d2dfPnycfPmTU6ePEn27Nn57bffgLh5MU2aNGHHjh14eHhQr149AM6ePcuNGzfIlSsXQUFB8d7Mp6XmzZtz/PhxtmzZwpdffkndunUxMzPj+PHj+Pr6UrNmTfr37//Ba4wYMYIDBw7g5eVF06ZNqV27NhUrVmTQoEHMnz+fZs2aUb9+fXLmzMmFCxe4fPkyBQoUYOzYsSnyGp4/f87x48fJkiULjRo1eu95JiYmtGnThoULF7Ju3Trc3d2pVq0a3bp1Y/Xq1Xz55ZfUq1cPMzMz9u3bx/Pnzxk/fjx58+YF4KeffqJLly58//337Nq1CwcHB+7fv8+RI0coUqQIo0aNAuJ+v3Qb9bZp0wZXV1f8/f31e0OdPXs2Sa+rUqVKFC1alOPHj9OlSxcqVKjAq1evOHr0KIGBgeTIkYOQkBBevnyJpaUl1tbW/PLLL3zzzTd06NABd3d38uTJw+nTp7lx4waNGjVKsJJg8+bNmTx5Mn5+fjRr1izeqp8fU6hQIdatW8fgwYP54Ycf8PT0pHr16mTPnp1nz55x7NgxQkJCqFWrFlOnTk10SKluTyrdXnD/XQkT4v5OXL58mVWrVnHmzBmqVq1KTEwM+/bt48WLF7Rt2zZJ+2EZuj59+nD69Gn27t3LzZs3qVmzJiYmJhw6dAg/Pz9cXV0TXYVViMxOeqiEELRt25aIiAiMjIzire6n0759e3799Vfy58/P9u3b2bhxI8HBwQwZMoTdu3djaWnJsWPHkr3S3+DBg5k1axb58+dn27ZtHD9+nA4dOiR4k6vRaPjzzz/p1KkTr169Ys2aNRw8eJBChQqxePFi/aa+uk/8IW4ZaVdXV+7du8fatWvfO8fK2tpa/4YsNjaW9evXc/ToUSpUqICnpydDhw5N1mv6HLo3mo6Ojvoehg9p164dq1evpmbNmpw8eZJVq1bh6+tL+/bt2bRpk374FcDPP//MwIEDURSFtWvXsnv3bqysrJg2bRpz5swB4MiRI6nyupLi999/57fffqNgwYLs3LmTLVu2YGNjw48//oinp2eCYVr/ZWVlxYQJEwD48ccf9T1xw4YNY8GCBZQtW5aDBw+yZs0aQkND6d27Nxs2bNAX3p9ry5YtxMbG0qhRo3gLXCRGN7/q5MmT+mXYf/jhB6ZOnUrBggX1v2P58+dn9uzZdO3aVf/c4sWL4+XlRYcOHbh79y6rVq3i+vXrdOjQgbVr18YrRubPn0+HDh0IDg5m1apV3L9/n19//ZUePXok+XVZWlqybNkymjdvjq+vL6tWreLUqVM4Ozvz119/0b17dyD+716TJk3466+/qFGjBidOnGD16tWEh4czcuRIZsyYkeAeVlZW1KlTB4j78Ca57O3t2b59O7/88gu5c+fm4MGDLFu2jAsXLlC1alUWLVqEp6dngvlp79L1KpYvX55ixYoleDxbtmysW7dOvzHx+vXr2b17N4ULF2bKlCn8/PPPKdbTqSZTU1OWLFnC2LFjsba2ZvPmzWzevBkbGxt++OEH/vzzz0SLUiEyO42i1qB5IYQQQgigYcOGxMTEcODAgfeu9iiEEIZK/moJIYQQQjWbN2/m0aNHdOjQQYopIUS6JD1UQgghhEhzffr0ISgoiFu3bpE7d2527tyZqntsCSFEapGPgoQQQgiR5nLlysXDhw9xdnZmyZIlUkwJIdIt6aESQgghhBBCiE8kPVRCCCGEEEII8YmkoBJCCCGEEEKITyQb+/4/rVZLTEwMRkZGGWIvCSGEEEIIIcSnURQFrVaLiYnJR1cglYLq/8XExHD16lW1YwghhBBCCCEMhLOz80c3tFa9oIqMjGTSpEns27cPCwsLevfuTe/evRM9d//+/cycORN/f38cHR354YcfKFOmjP7xFStWsHTpUsLCwmjSpAnjx4/H0tIySTl0laezszPGxsaf/8I+U2xsLFevXjWYPJmBtLk6pN3VIe2uDml3dUi7q0PaXR3S7ilD145J2R9P9YJq6tSpXLt2jZUrV/L06VPGjBlD/vz5ady4cbzz7ty5w6hRo/jpp5+oWLEiK1asYMCAAezfvx9LS0v27t3LH3/8wbRp08iZMydjx45l2rRp/Pjjj0nKoRvmZ2xsbFA/fIaWJzOQNleHtLs6pN3VIe2uDml3dUi7q0PaPWUkZSqQqotShIeHs3HjRr7//nvKlClDgwYN6Nu3L3/99VeCc0+ePEnJkiVp1aoVhQsXZuTIkQQGBnL37l0AVq1aRY8ePahXrx4uLi5MmjSJTZs2ERERkdYvSwghhBBCCJFJqFpQ+fj4EBMTQ4UKFfTHKlWqxJUrV9BqtfHOtbGx4e7du1y4cAGtVouXlxdWVlYULlxY3yVXuXJl/fnly5cnOjoaHx+fNHs9QgghhBBCiMxF1SF/gYGB5MiRI95Er1y5chEZGcnLly+xtbXVH2/atCmHDh2ic+fOGBsbY2RkxKJFi8iePTshISFERkaSO3du/fkmJibY2Njg7++frEyxsbGf/8JSgC6HoeTJDKTN1SHtrg5pd3VIu6tD2l0d0u7qkHZPGclpP1ULqoiIiASrZui+joqKinc8JCSEwMBAfvzxR8qVK8fatWsZO3Ysmzdv1p+b2LX+e52PMbSV/gwtT2Ygba4OaXd1SLurQ9pdHdLu6pB2V4e0e9pRtaAyNzdPUPDovrawsIh3fPr06djb29OlSxcAfv75Z5o0acKmTZto27ZtvOe+e62krvKnYygrosgKLWlP2lwd0u7qkHZXh7S7OqTd1SHtrg5p95Sha8ekULWgypMnDyEhIcTExGBiEhclMDAQCwsLsmXLFu/c69ev061bN/3XRkZGODo68vTpU2xsbDA3NycoKIgSJUoAcftKvXz5Ejs7u2RlMrQVUQwtT2Ygba4OaXd1SLurQ9pdHdLu6pB2V4e0e9pRdVEKJycnTExMuHz5sv7YhQsXcHZ2TrDme+7cubl37168Yw8ePKBgwYIYGRnh7OzMhQsX9I9dvnwZExMTHB0dU/U1CCGEEEIIITIvVQsqS0tLWrVqxcSJE/H29ubAgQMsW7aM7t27A3G9VW/fvgWgffv2bNiwgS1btvDo0SOmT5/O06dPad26NQCdO3dm6dKlHDhwAG9vbyZOnEj79u2TPeRPCCGEEEIIIZJK9Y19x44dy8SJE+nRowdWVlYMHTqUhg0bAlCrVi0mT56Mh4cHTZs25c2bNyxatAh/f3+cnJxYuXIlOXPmBKBZs2b4+fnx448/EhUVRcOGDRk9erSaL00IIYQQQgiRwaleUFlaWjJlyhSmTJmS4LFbt27F+7pdu3a0a9fuvdfq378//fv3T/GMQgghhBBCCJEYVYf8CSGEEEIIIUR6JgWVEEIIIYQQQnwiKaiEEEIIIYQQ4hNJQSWEEEIIIYQQn0gKKiGEEEIIIYT4RFJQCSGEEEIIIcQnkoJKCCGEEB8V/CaYq0+uqh1DCCEMjhRUQgghhPio9ovaU+6nchy/fVztKEIIYVCkoBJCCCHEB4W8CeGwz2EURWH+kflqxxFCCIMiBZUQQgghPujo7aNoFS0AXpe8CHodpHIiIYQwHFJQCSGEEOKDDt48qP//UTFRrDmzRsU0QghhWKSgEkIIIcQHHfSJK6jcndwB8DzuiaIoakYSQgiDIQWVEEIIId7r6cun3Hx2E41Gw+Jui7E0s+T60+ucuX9G7WhCCGEQpKASQgghxHsd9jkMQIVCFShmV4x2ldoB4HnCU81YQghhMKSgEkIIIcR76Yb7uTm5AdC3Vl8A1p1bx+u3r1XLJYQQhkIKKiGEEEIkSlEU/YIUbo5xBVWtUrVwyOvAm8g3rDu7Ts14QghhEKSgEkIIIUSi7gXe43HwY0yNTalVqhYAGo1G30slw/6EEEIKKiGEEEK8h653qnqJ6mQ1z6o/3r16d0yMTTj74CzeT7zViieEEAZBCiohhBBCJOqQzyEA6jvUj3c8d7bctCzXEoClJ5ameS4hhDAkUlAJIYQQIgGtVqsvqHQLUryrb+24YX+r/13N2+i3aZpNCCEMiRRUQgghhEjgqt9VgsKCyGqelarFqiZ4vEHpBhS2LUxIeAheF71USCiEEIZBCiohhBBCJKCbP+VayhUzE7MEjxsbGdO7Zm8API/L4hRCiMxLCiohhBBCJPDf/acS06tmLzQaDYdvHebu87tpFU0IIQyKFFRCCCGEiCc6Jppjt48BUN+x/nvPK5yzMI3KNAJg2YllaZJNCCEMjRRUQgghhIjn3MNzhEWGkdMqJ+UKlvvgubo9qZafWk50THRaxBNCCIMiBZUQQggh4tEN96vnUA8jow+/Vfiy3Jfkts6Nf6g/u67tSot4QghhUKSgEkIIIUQ8ugUp3BzfP39Kx8zEjB41egCyOIUQInOSgkoIIYQQeuGR4fx7/1/gwwtSvKtPrT4A7Lq6C78Qv1TLJoQQhkgKKiGEEELonbx3kqiYKArmKEjJ3CWT9ByHvA7ULlUbraJlxakVqRtQCCEMjBRUQgghhNB7d7ifRqNJ8vN0i1MsPbEUrVabKtmEEMIQSUElhBBCCD19QZXE4X46bSu1Jbtldh4EPeCQz6HUiCaEEAZJCiohhBBCABDyJoQLjy8AyS+osphnocsXXQDwPCGLUwghMg8pqIQQQggBwJFbR1AUBce8juS3yZ/s5/etHTfsb/OlzQS9DkrpeEIIYZCkoBJCCCEEgH6oXn3H+p/0/AqFK1CxcEWiYqJYfXp1SkYTQgiDJQWVEEIIIYD/beib3OF+79L1Unke90RRlBTJJYQQhkwKKiGEEELw9OVTbj67iUajoa5D3U++TueqnbE0s+TGsxucvn865QIKIYSBkoJKCCGEEPrhfhULV8Q2q+0nXyd7luy0r9QeiOulEkKIjE4KKiGEEELE23/qc+mG/a07t45XEa8++3pCCGHIpKASQgghMjlFUT57QYp31SxZE4e8DoRHhbPu3LrPvp4QQhgyKaiEEEKITO5e4D0eBz/G1NiUWqVqffb1NBoNfWv9b3EKIYTIyKSgEkIIITI53XC/6iWqk9U8a4pcs3v17pgam3Lu4Tmu+F5JkWsKIYQhkoJKCCGEyORScv6UTu5suWlZviUAS08sTbHrCiGEoZGCSgghhMjEtFoth27FzZ/6nP2nEqMb9rf69GoioiJS9NpCCGEopKASQgghMrGrfld5EfaCrOZZqVK0Sope2720O4VtC/My/CVeF71S9NpCCGEopKASQgghMjHdcD/XUq6YmZil6LWNjYzpXbM3AJ4nZHEKIUTGJAWVEEIIkYkd9Pn/+VMpPNxPp3et3mg0Go7cOsKdgDupcg8hhFCTFFRCCCFEJhUdE82x28eAlF2Q4l2FbAvRuExjAJadXJYq9xBCCDVJQSWEEEJkUmcfniUsMoycVjlxKeiSavfpWztucYoVp1YQHROdavcRQgg1SEElhBBCZFKHfOJW96vnUA8jo9R7S9DcpTm5rXPjH+rPzqs7U+0+QgihBimohBBCiEwqNfafSoyZiRk9avQAwPO4LE4hhMhYpKASQgghMqHwyHD+vf8vkHoLUrxLtyfV7mu7eRL8JNXvJ4QQacVE7QBCCCGESHsn7p4gKiaKQraFKJm7ZKrfzz6vPa72rhy7fYwVp1bwQ/MfUv2eQmRGD4IecPD+Qe5r76fqUN7U4lLQhVJ5SqkdI1mkoBJCCCEyoXeH+2k0mjS5Z99afTl2+xhLTyxlXNNx6fLNnhCG7Pmr53wx+QuC3wSrHeWTWZlb8WL2ixTfFy81SUElhBBCZEK6BSnqO9ZPs3u2qdiGoWuH8vDFQw76HKRB6QZpdm8hMoORG0YS/CaYXFly4ZjfMc0+LElJNUvWTFfFFEhBJYQQQmQ6IW9CuPD4ApA286d0sphnocsXXZh/ZD6exz2loBIiBe2/sZ+/zvyFkcaImY1n0rlhZ4yNjdWOlSmo3tceGRnJuHHjqFy5MrVq1WLZssQ3/evWrRsODg4J/hs7diwAoaGhCR774osv0vKlCCGEEOnCkVtHUBQFx7yO5LfJn6b37le7HwCbL20m6HVQmt5biIwqIiqCr9Z8BcCQekMonbu0yokyF9V7qKZOncq1a9dYuXIlT58+ZcyYMeTPn5/GjRvHO2/evHlER/9vM8ArV64wfPhwOnfuDMDdu3exsbFhx44d+nNkbLYQQgiR0EGf/58/lYa9UzrlC5enUpFKXHh0gdWnVzOiwYg0zyBERvPLzl+4F3iPAjYFmNRiEvd87qkdKVNRtaAKDw9n48aNLFmyhDJlylCmTBnu3LnDX3/9laCgsrGx0f//2NhYZs2aRd++fXF2dgbg/v37FCtWDDs7u7R8CUIIIUS6k1b7T71P31p9ufDoAp7HPRnuPjxdzvMQwlBc97vO1L1TAfij8x9YW1irnCjzUbULx8fHh5iYGCpUqKA/VqlSJa5cuYJWq33v87y8vAgNDaVfv376Y3fv3qVo0aKpGVcIIYRI956+fIqPvw8ajYY6DnVUydCpaicszSy58ewG/977V5UMQmQEWq2WAWsGEBMbQ6vyrWhVoZXakTIlVXuoAgMDyZEjB2Zm/1vJI1euXERGRvLy5UtsbW0TPEdRFDw9PenevTtZs2bVH7937x4xMTG0bduWgIAAKleuzNixY8mdO3eyMsXGxn76C0pBuhyGkiczkDZXh7S7OqTd1WEI7X7gxgEAKhaqSHaL7KpksTK3ol3Fdqw6vYolx5fwRbHUnfNsCO2eGUm7p74lx5dw8u5JrMytmNV+FrGxsdLuKSQ57adqQRURERGvmAL0X0dFRSX6nDNnzuDv70/79u3jHb9//z62traMHTsWRVGYNWsWAwcOZOPGjcla4eTq1avJfBWpy9DyZAbS5uqQdleHtLs61Gz3f079A0AZ2zJcvnxZtRyueVxZxSrWnV1HT8eeWJlZpfo95eddHdLuqSMoPIhvN34LwIBKA3jx+AUvHr/QPy7tnnZULajMzc0TFE66ry0sLBJ9zt69e3F1dY03pwpg586daDQa/fPmzp1LrVq1uHLlChUrVkxyJmdnZ4NYYjI2NparV68aTJ7MQNpcHdLu6pB2V4fa7a4oCpfXXwago2tHypcun+YZdMqVK8f0M9Px8ffhZuRN+lXt9/EnfSK12z2zknZPXV2XduV11GsqFa7Eb11/w9goro2l3VOGrh2TQtWCKk+ePISEhBATE4OJSVyUwMBALCwsyJYtW6LPOX78OEOGDElw3NLSMt7XOXPmxMbGhoCAgGRlMjY2NqgfPkPLkxlIm6tD2l0d0u7qUKvd7wTcwTfEF1NjU+rY11H9e9+3dl++2fgNy04uY2Ddgal+P/l5V4e0e8rbc20P686tw0hjxOLuizEzTbgRrrR72lF1UQonJydMTEziDTm4cOECzs7OiS55HhwcjK+vL5UqVYp3PCwsjCpVqnD69Gn9sYCAAEJCQihevHiq5RdCCCHSk0M+hwCoXqI6WcyzqJwGulXrhqmxKecenuOK7xW14wiRLoRHhjPor0EADHMbRsUiSR+JJVKHqgWVpaUlrVq1YuLEiXh7e3PgwAGWLVtG9+7dgbjeqrdv3+rPv3PnDubm5hQsWDDedaysrKhUqRKTJ0/G29ub69evM2LECGrXro2Dg0OaviYhhBDCUKm9XPp/5c6Wm5blWwLgedxT5TRCpA8/7/yZB0EPKGRbiJ9a/qR2HIHKBRXA2LFjKVOmDD169GDSpEkMHTqUhg0bAlCrVi127dqlP/fFixdky5Yt0f0qpkyZQunSpenfvz/dunWjQIECTJ8+Pc1ehxBCCGHItFoth27F9VCpsaHv+/SrHTd3as2ZNURERaicRgjDdvXJVabvi3t/+2fnP7GySP3FXMTHqTqHCuJ6qaZMmcKUKVMSPHbr1q14Xzdt2pSmTZsmep3s2bMzefLkVMkohKF7G/2W8w/PU6NEjUSHywohhPcTb16EvcDK3IqqRauqHUfP3cmdIjmL8OjFI7wuetGlWhe1I4kU4uPvw93gu5SnvNpRMgStVkv/1f2JiY3Bo6IHX5b7Uu1I4v/JOy8h0rngN8G4TnWl9tTa/HH4D7XjCCEM1EGfuOF+rvaumJqYqpzmf4yMjOhdszcAnidk2F96pigK5x+eZ5zXOJzGO1F2Ylk6bejEDu8dakfLEBYfW8zp+6extrBmbse5ascR75CCSoh07Pmr59SbXo9zD88BMH3fdKJjolVOJYQwRLoFKQxl/tS7etXshUaj4citI9wJuKN2HJEMsdpYjt46yrB1wyjyXRGq/FqFybsn4+PvA4CCQs8VPbn3/J7KSdO3Zy+f8Z3XdwD81vo3CuQooHIi8S4pqIRIp/xC/KgzrQ7eT7zJmz0vuaxy4Rvsy6aLm9SOJoQwMNEx0Ry7fQyA+o71VU6TUCHbQjQu0xiApSeWqpxGfExkdCS7ru6i36p+5PsmH3Wn12Xuwbn4BvuS1Twr7Sq1Y22/tfhP98cljwsvw1/SZkEbwiPD1Y6ebg1fP5zQiFCqFK3CV3W/UjuO+A8pqIRIhx4GPcR1mis+/j4Usi3EsdHHGFp/KAAz9s1AURSVEwohDMnZh2cJiwwjl1UuXAq6qB0nUbrFKVacWiE97QYo7G0YG89vpPOSztiNtKPZ3GZ4Hvck8HUgtllt6VmjJ1sHbyVwZiAbBm6gY9WO5LLKxe8Nfie3dW6uPLnCgDUD5N+nT7Dr6i42nN+AsZExi7st1m/gKwyH6otSCCGS57b/bdxmuvEk5Akl7EpwcNRBiuQswld1v2Ly7smcf3SeE3dOUNu+ttpRhRAGQrdcej2Hega7cE1zl+bkts5NwKsAdl7dSasKrdSOlOkFvwlm+5XteF30Yu/1vUTGROofy2+Tn1blW+FR0QPXUu+fl5fbKjd/9/2bRnMaseb0GqoXr86geoPS6iWke28i3+j3nBrhPoLyhcurG0gkyjD/qgohEnXN7xqu01x5EvIEp3xOHPv2GEVyFgHAztqO7tXj9nCbsX+GmjGFEAZGtyCFIS2X/l+mJqb0rNETkD2p1PT05VPmH56P+0x3co/MTc/lPdl2ZRuRMZGUsCvB6Eaj+fe7f/Gd4sufXf7Ezcnto4uc1HWoy5Q2cas5D18/nH/v/ZsWLyVDmLR9Eo9ePKKwbWEmtpiodhzxHtJDJUQ6cfHRRRrObsiLsBeUK1iO/SP3Y2dtF++cEe4jWHxsMduubONOwB1K5SmlUlohhKEIjwzXv4E15IIKoE+tPkzdO5Xd13bzJPgJBW0Lqh0pU7j7/C6bL23G66IXp++fjveYS0EXPCp44FHRg7IFyia6F2hSjGwwkjP3z7DxwkbaLmzLxfEXyZMtT0rEz7Cu+F5h5v6ZQNyeU1nNs6qcSLyPFFRCpAOn7p6iydwmvIp4RdViVdkzbA85suZIcJ5jPkeaOTdj59WdzD4wmz+7/KlCWiGEITlx9wTRsdEUsi1ECbsSasf5IPu89rjau3Ls9jGWn1rO+Obj1Y6UISmKwlW/q3hd9GLzpc14P/GO93j1EtXxqOBB6wqtKZE7ZX5mNBoNS3su5drTa9x8dpMOizpwYOQBTIzlrWhiYrWxDFg9gFhtLG0rtaV5ueZqRxIfIEP+hDBwh24eouHshryKeEXtUrXZP2J/osWUzqiGowBYfmo5wW+C0yqmEMJA6eZPuTm6fXLvQlrqW6svELfan1arVTlNxqHVavn33r+M3jiaUt+XotykckzaPgnvJ94YGxnj7uTOn53/xG+aH6e+O8U3jb5JsWJKx9rCGq+vvLC2sObo7aP6ZcBFQguPLuTMgzNks8zGnI5z1I4jPkIKKiEM2O6ru2k2rxlvIt/QsHRD9gzbQzbLbB98Tl2HulQoXIGIqAgWHlmYRkmFEIYqPcyfelfbSm3JbpmdRy8e6bOLTxMdE83BmwcZ/NdgCn5bkBq/12D6vuncC7yHhakFLcq1YEWvFTyf+Zz9I/czqN4g8tvkT9VMjvkcWdFrBRC3Ku3G8xtT9X7p0dOXTxnrNRaAya0np/r3RHw+KaiEMFBeF71o+WdL3ka/pUW5Fmwbso0s5lk++jyNRsPIBiMBmHd4HpHRkR95hhAiowp+E8zFxxcBw9x/KjGWZpZ0rdYVkMUpPkVEVATbLm+j57Ke5BmVB/eZ7sw/Mp9noc+wtrCmU9VObBy4kcCZgWwdspUeNXpgm9U2TTN6VPTg20bfAtBrRS9uPL2Rpvc3dMPWDeP129d8UewLBtQZoHYckQRSUAlhgP46/RftF7UnOjaaDlU68M/AfzA3NU/y89tXbk8BmwL4h/qz7ty6VEwqhDBkR28dRVEUnPI5patPuXXD/jZf2kzg60CV0xi+VxGvWHtmLe0WtsNupB0t/2zJyn9XEhIeQi6rXPSt3ZddX+8icGYgf/f7m7aV2mJlYaVq5l9b/0p9x/q8iXyDxwIPXkW8UjWPodhxZQf/XPgnbs+p7rLnVHohBZUQBmbJsSV0W9aNWG0sPWv05K++f310Sdr/MjMx02/0O3P/TNlIUYhMSjdkLr30TumUL1yeSkUqER0bzep/V6sdxyA9f/Ucz+OeNJ3TFLuRdnT27Mw/F/7hTeQbCtkW4mu3rznyzRGeTX/Gku5LaOLcJFkfzKU2E2MT1vZbS8EcBbnlf4teK3pl+n+rwt6GMfjvwQCMajDKYDfhFglJQSWEAZlzYA79V/dHURQG1R3E0h5LP/nTqf6u/clqnhXvJ976SelCiMzl3QUp0htdL5XnCc9M/0Zb5/GLx8w5MIe60+qS75t89FvVj93XdhMVE4VDXgfGNhnLue/P8ej3R8zpOIc6DnUMehW93Nlys+mrTZiZmOF10Ytpe6epHUlVE7dP5HHwY4rmLMqPX/6odhyRDFJQCWEgJu+azPD1wwEY3Wg0f3T+AyOjT/8VzZE1B71r9gZko1+R0Ou3r/H29/74iSLd8gvxw8ffByONEXUd6qodJ9k6f9GZLGZZuPnsZqbeCPbpy6dM3jWZKr9Uoch3RRi+fjhHbx9Fq2ipWLgiv7T6hRs/3cDnZx9+8/iNykUrp4vVHHWqFqvK3I5zARjrNZZDNw+pnEgdlx5fYvaB2QDM7zJf9pxKZ6SgEkJliqLww+YfGLd5HAATv5zIlDZTUuQfxOHuw9FoNOy5tofrftc/+3oiY3gS/IRqk6vRe0tvNl3YpHYckUoO+cS9Ma1YuOIHt1owVNkss9G+cnsgrpcqM7r8+DLlJpVj3OZxnH90Ho1GQ+1StZnVYRYPJj/gwvgLfN/se5zyOakd9bP0d+1Pzxo90SpaOizugG+wr9qR0lSsNpb+q/oTq42lQ5UONHFuonYkkUxSUAmhIkVRGLlhJL/u+hWAqW2nMqHFhBT7dLG4XXFaV2gNwKwDs1LkmiJ9exD4ANdprtwKuAXAgmMLVE4kUouuoEovy6Unpm/tuGF/68+tz3SLFpy5f4Z6M+oRFBZE2QJlWdRtEc+mP+PYt8cY7j6cormKqh0xxWg0GuZ3mU+FwhUICgui7cK2mWqF2vmH53P+0XmyW2ZndofZascRn0AKKiFUotVq+WrNV/ou/j86/8HoRqNT/D6jGsRt9Lvm9BoCXgWk+PVF+nHb/zau01x5EPSAIrZF0KDhyK0j3H1+V+1oIoUpipJuF6R4V40SNXDM60h4VDhrz65VO06aOXb7GO4z3XkZ/pIaJWpw4tsT9HftT55sedSOlmoszSzZNHATObLk4OyDswxbN0ztSGniSfATvt/yPQBT2kwhb/a8KicSn0IKKiFUEBMbQ8/lPVl0bBFGGiOW9VzG4HqDU+Ve1UtU54tiXxAZE8n8w/NT5R7C8F19chXXaa48CXmCUz4njn97nOqFqgOw7MQyldOJlHb3+V18g30xMzGjVslaasf5ZBqNRt9LlVn2pNp3fR+N5zQmLDKM+o712Tt8L9mzZFc7VpooZleMv/v9jUajYdGxRSw/uVztSKnu63Vf8/rta6qXqE6/2v3UjiM+kRRUQqSxqJgoOi3pxOrTqzE2Muavvn/Rq2avVLufRqNhVMO4Xqr5R+YTERWRavcShun8w/PUnV6XgFcBlCtYjqOjj5LfJj+tnFoBsPzUcqJjotUNKVKUbnW/6sWrJ2lDcEPWvXp3TI1NOf/oPJcfX1Y7TqraenkrX/7xJRFRETR1bsqOoTtU3y8qrTUu25hJLSYB8NWar7j46KLKiVLP1stb2XxpMybGJizquuizFqIS6pLvnBBp6G30Wzzme/DPhX8wMzFj01eb6Fi1Y6rft3WF1hTJWYSgsCBWn5Y9XTKTk3dP4jbTjeA3wVQtVpXD3xzGztoOgNpFapPbOjf+of7surZL5aQiJemG+6Xn+VM6dtZ2tCrfCoClJ5aqGyYVrT+3njYL2hAVE4VHRQ82D9qMpZml2rFU8X3T72nu0pzImEjaLGjDi7AXakdKca/fvmbI30MA+KbhNzgXdFY5kfgcUlAJkUbeRL6h+bzm7Ly6E0szS7YN3kbL8i3T5N4mxiYMdx8OwKz9s9BqtWlyX6GuQzcP0XBWQ15FvKJ2qdrsH7E/3mpvpsamdKvWDcg8w6kyA61Wy+Fbh4H0uf9UYnTD/tacWZMhe9lXnFxB5yWdidXG0rVaV9b3X4+ZiZnasVRjZGTE6j6rKWFXgocvHtLFswux2li1Y6WoH7f+yJOQJxS3K874ZuPVjiM+kxRUQqSB0PBQGs1uxMGbB7Eyt2LPsD00KtsoTTP0rtmbbJbZ8PH3Yfe13Wl6b5H2dnrvpOncpoRHhdOwdEP2DNtDNstsCc7rU6sPALuu7sIvxC+tY4pU4P3EmxdhL7Ayt6JK0Spqx0kR7k7uFMlZhJfhL9l0MWMt9T//8Hx6reiFVtHSr3Y/VvZaadCb8aYVmyw2eA3ywtLMkr3X9zJp+yS1I6WYC48uMPdg3N5b8zvPT/fDcoUUVEKkuhdhL3Cf6c7JuyexyWLDgZEHcLV3TfMc2Syz0b92fwBm7p+Z5vcXaWfThU20nt+ayJhIWpRrwbYh2977D7Z9Hntc7V3RKlpWnFqRtkFFqtAN93O1d8XUxFTlNCnDyMhIv1F5RupNnbZ3GoP/jluQaJjbMBZ1k3k073Ip6MLibosB+HnHz+y4skPlRJ8vJjaG/qv6o1W0dKraKc0/XBWpQ35rhUhFAa8CqDu9LucfnSeXVS4OjzrMF8W/UC3P0PpDMTYy5pDPoQw/uTuzWnN6De0XtSc6NpoOVTrwz8B/MDc1/+Bz+taKG0619MRSGQ6aAegWpMgow/10etXshZHGiKO3j3Lb/7bacT6LoihM2jaJb//5FoBxTccxq8OsFNuDMCPpWq0rQ+rFzTXqurRrut/m4Y/Df3Dx8UVsstgwq4PsD5lRSEElRCp5EvwE16muXPO7Rr7s+Tg6+ijlC5dXNVPhnIVpX7k9IL1UGdHiY4vpvqw7WkVLzxo9+avvX0nqoWhTsQ3ZLbPzIOiBfjNYkT5FxURx7M4xIGMsSPGuQraFaFy2MQDLTqbfpf4VRWHMpjFM3D4RgF9b/cqvrX+VYuoDZrSfQY0SNQiNCMVjvgfhkeFqR/okvsG+/LDlBwCmtpmaofcVy2ykoBIiFTwIfIDrNFduB9ymsG1hjn17jNL5S6sdC4CRDUYCsPbcWpkzk4HMOTCHAasHoCgKg+oOYmmPpRgbGSfpuVnMs9Dliy4AeJ7IOMOpMqNzD8/xJvINuaxy4Vwg460aputNXXFqRbpc6l+r1TJ07VCm7Z0GwKwOsxjXbJzKqQyfmYkZGwduJE+2PFz1u8qANXF/69KboWuH8ibyDTVL1tTPXxUZgxRUQqSwW/63qD21Ng+CHlAyd0mOf3uckrlLqh1Lr3LRyrjauxITG8Mfh/9QO45IAb/t/I3h64cDMLrRaP7o/Eey52HoNpTcfGkzQa+DUjqiSCO64X71HOplyLk4zV2ak9s6NwGvAtjhnb7m08RqY+m7qi9/Hv4zbuPabov0q6+Kj8tvk5/1/ddjbGTMmtNr+PPwn2pHSpbNFzez9fJWTI1NWdxtcYb8/czM5LspRAryfuKN61RX/F76UTpfaY6NPkbhnIXVjpXAqAZxG/0uPLqQsLdhKqcRn0pRFL7f/D3fb/kegIlfTmRKmymfNHSofOHyVCpSiaiYKNacWZPSUUUayUj7TyXG1MSUnjV6AumrNzU6Jpounl1YfnI5RhojVvVeRX/X/mrHSnfqONRhWtu43r0RG0Zw6u4plRMlzauIVwxdOxSAbxt9azAjVkTKkYJKiBRy/uF56k6ry/PXzylfqDxHRh8hn00+tWMlqrlLc0rlLsXL8JcsP7lc7TjiEyiKwoj1I/ht128ATG07lQktJnzWPAzdcKolx5aky+E0md2byDf8e+9fIOMWVPC/Pan2XNuDb7Cvymk+LjI6knaL2rH+3HpMjU3ZMGADXat1VTtWujXcfTjtK7cnJjaGtgvb4h/qr3akjxq/dTx+L/0ombsk3zf7Xu04IhVIQSVECjh59yRuM90ICQ+hWvFqHP7mMHbWdmrHei8jIyNGNBgBwOyDszPchokZnVarZeCagcw5OAeAPzr/wehGoz/7up2qdsLSzJIbz25w+v7pz76eSFsn7pwgOjaawraFKWFXQu04qaZUnlLUsa+TLpb6D48Mp8UfLdh6eSvmJuZsGbyFNpXaqB0rXdNoNCztsZTS+UrzLPQZHRZ3MOj5dOcenGPeoXkALOiyAEszS5UTidQgBZUQn+ngzYM0nNWQVxGvqOtQl30j9mGTxUbtWB/Vo3oPbLPacj/wPlsvb1U7jkiimNgYeizvweJjizHSGLGs5zIG1xucItfOniU77SvFrQKZkfb6ySx0KzS6Obll+BXjdL1UhrzU/+u3r2kytwn7buwji1kWdn69k6bOTdWOlSFYWVjhNcgLawtrjt0+xphNY9SOlKiY2Bj6r+6Poih0rdYV99LuakcSqUQKKiE+w07vnTSb24zwqHAalWnEzqE7sbawVjtWkmQxz8JXdb4CZAn19CIqJoqOizuy5vQajI2M+avvX/Sq2StF79HPNW5xinXn1vEq4lWKXlukLt38qfqO9VVOkvp0S/0/evGIAzcPqB0ngZA3IbjPdOfY7WNks8zGvhH7MvQwTDU45HVgZa+VAMw6MIv159arnCihuQfnctn3Mjmy5GBGuxlqxxGpSAoqIT7RPxf+odX8VkTGRNKqfCu2Dt5KFvMsasdKlsH1BmNmYsbJuyc5c/+M2nHEB0RERdB6fms2XdyEmYkZm77aRMeqHVP8PjVK1MAxryPhUeEG+QZFJC74TTAXH18EMkdBZWlmqZ+HZGi9qYGvA6k/oz5nH5zFNqstB0cepGbJmmrHypBaV2zNd02+A6DPyj5c97uucqL/efTiEeO3jgdgWttp5M6WW+VEIjVJQSXEJ1j972o6LOpATGwMnap2YsOADZibmqsdK9ny2eSjc9XOgPRSGbKwt2E0n9ecXVd3YWlmybbB22hZvmWq3Euj0eiHUy05viRV7iFS3pFbR1AUBad8TuS3ya92nDShW+p/y+UtBL4OVDlNnKcvn1JnWh0u+14mT7Y8HPnmCJWLVlY7Vob2c8ufcXNy403kGzwWeBAaHqp2JBRFYcjfQwiPCsfV3pXetXqrHUmkMimohEimRUcX0WN5D7SKlj61+rC6z2pMTUzVjvXJdItT/HPhHx4GPVQ3jEggNDyURrMbccjnEFbmVuwZtodGZRul6j27VeuGqbEp5x6e44rvlVS9l0gZuv2n3Bwzz7CycoXKUblIZaJjo1n972q14/DoxSNcp7py89lNCtgU4OjoozgXzHibKxsaE2MT1vZbSyHbQtwOuE3P5T1VX6XU66IXO7x3YGpsysKuCzP8nEYhBZUQyTJr/ywGrhmIoigMrT+Uxd0WY2xkrHasz+JS0IUGpRugVbTMPThX7TjiHS/CXuA2041T905hk8WGAyMP4Grvmur3zZ0tt74HbOmJpal+P/H53l2QIjN5tzdVzTfRdwLuUHtqbe4F3qNYrmIc//Y4DnkdVMuT2dhZ2/HPwH8wMzFjy+UtTNkzRbUsoeGh+j2nvmvyHU75nFTLItKOFFRCJNGvO39l5IaRAIxpPIY5HedkmJ3ORzaIe12eJzwNYriEAP9Qf+pOr8uFRxfIZZWLw6MO80XxL9Ls/rrhVKtPryYiKiLN7iuSzy/EDx9/H4w0RtSxr6N2nDTVqWonsphlwcffh1P31Nnk9brfdVynueIb7ItDXgeOjT5GMbtiqmTJzKoWq8q8TnHLk3+/+XsO3FBnsZLvt3zPs9BnlMpdinFNx6mSQaS9jPFuUIhUpCgK47zG8cOWHwD4qeVPTPaYnKG68BuVaUTpfKV5/fY1nicMa4J3ZvQk+Al1ptXhmt818mXPx9HRRylfuHyaZnB3cqdIziK8DH/J5kub0/TeInl0vVMVC1ckR9YcKqdJW9kss9G+snpL/V96fIk60+vgH+qPcwFnjo4+SkHbgmmeQ8TpV7sfvWv2Rqto6bSkE49fPE7T+5+5f4b5R+YDsLDrQixMLdL0/kI9UlAJ8QGKojB8/XAm754MwIx2MxjffHyGKqYgbiECXS/VnINzDHqTxIzufuB9ak+tze2A2xS2Lcyxb49ROn/pNM9hZGRE75pxE6llcQrDpp8/lcmG++noelM3nN+Qpj3sp++dpt70erwIe0HlIpU5MvoIebLlSbP7i4Q0Gg1/dP6DioUrEhQWRNuFbXkb/TZN7h0dE63fc6p79e7Ud8r4q22K/5GCSoj3iNXG0n91f/28ovld5jOy4UiVU6WeLtW6kNs6N77Bvmy6uEntOJmSzzMfXKe68vDFQ0rmLsnxb49TMndJ1fL0qtkLjUbDkVtHuBNwR7Uc4v0URdHvP5VZC6rqJarjlM+J8Khw1p1blyb3PHLrCA1mNSA0IpSaJWtyYOQBbLPapsm9xYdZmlmy6atN2Ga15dzDcwxbNyxN7jv74Gy8n3iT0yqn7DmVCUlBJUQiYmJj6L60O57HPTHSGLGi1wq+qvuV2rFSlYWpBYPrDQZgxr4Zqq+SlNl4P/HGdZorfi/9KJ2vNMdGH6NwzsKqZipkW4jGZRoDsOzkMlWziMTdfX6XJyFPMDMxo2aJzLnXkUajoW+ttFvqf8+1PTSZ04SwyDDcndzZO3wv2bNkT/X7iqQrmqsof/f9G41Gw+Jji1l2InX/fj0MesiEbRMAmN52Ormsc6Xq/YThkYJKiP+Iiomiw+IO/H32b/1yrD1q9FA7Vpr4qu5XWJhacP7ReU7cOaF2nEzj3INz1J1Wl8DXgVQoXIEjo4+Qzyaf2rGA/w2nWnFqhQwFNUC64X7Vi1dPdxuLp6Ru1eOW+r/w6AKXHl9KtftsubSFFn+04G30W5q7NGf70O1kNc+aavcTn65R2Ub81OInAAb9NYgLjy6kyn0URWHw34OJiIqgrkPdTPN+QcQnBZUQ73gb8xaPBR54XfTCzMQMr6+8aF+lvdqx0oydtR09qsf9YzBjvwxZSAsn7pzAbaYbIeEhVCtejUOjDmFnbad2LL3mLs3JbZ0b/1B/dl3bpXYc8R+Zfbifjp21Ha3KtwJSb6n/tWfW0nZhW6Jjo2lXqR2bvtokiw4YuHFNx/FluS+JjImkzYI2vAh7keL32Hh+I7uu7sLMxEz2nMrEpKAS4v89f/Wc4buGs+f6HizNLNkxdAdflvtS7Vhpbrj7cAC2Xdkm82ZS2YEbB2g0uxGv376mrkNd9o3Yh00WG7VjxWNqYkrPGj0BWHJMFqcwJFqtlsO3DgOZa0Pf9+nnGtebuub0mhRf6n/ZiWV0WdqFWG0s3ap14+9+f2NmYpai9xApz8jIiFW9V1HCrgSPXjyi85LOxGpjU+z6L8NfMmx93BytcU3Gyd5jmZgUVCJTe/ziMXMOzKHutLoUHFOQ80/PY21hzd5he2lQuoHa8VThmM+R5i7NURSF2Qdmqx0nw9pxZQfN5zUnPCqcRmUasXPoTqwtrNWOlag+tfoAsPvabp4EP1E5jdC58uQKL8JeYGVuRZWiVdSOozo3RzeK5CxCaERoii6sM+/gPPqs7IOiKAxwHcCKXiswMTZJseuL1GWTxQavQV5Ymlmy78Y+Jm6bmGLXHrd5HP6h/tjnsee7Jt+l2HVF+iMFlch0fJ75MHnXZKr8UoUi3xVh+PrhHL19FK2ixcnOiX3D91HbvrbaMVWlW0J9+anlqTJEIrPbeH4jrRe0JjImklblW7F18FaDnv9in9ceV3tXtIqWFadWqB1H/D/d/lN17OtgamKqchr1GRkZ6Yv/lFqcYsruKXy97msARriPYEHXBRlmQ/fMxKWgC0u6xf1M/LLzF7Zd3vbZ1/z33r8sPLoQgEXdFmFuav7Z1xTpl/xVEBmeoihcfHSRHzb/QOkfS+P0oxPjNo/j/KPzaDQaapeqzawOs7j7y11Wt1ktn/QCdR3qUqFwBSKiIlh0dJHacTKUVadW0XFxR2JiY+hUtRMbBmxIF/8Q6xanWHpiKVqtVuU0Av63IEV9R9nvRqdnjZ4YaYw4dvsYt/1vf/J1FEVhwtYJfOcV1+swvvl4ZrSfIfNj0rEu1bowtP5QALot6/ZZQ9rf3XOqV81e1HWom0IpRXolBZXIkGK1sRy/fZwR60dQbGwxKv1SiV93/crNZzcxNTalcdnGLO62mGfTn3Hs22MMdx9O0VxF1Y5tMN7d6Hfe4XlERkeqnChjWHR0ET2W90CraOlTqw+r+6xONz0LbSq2Ibtldh6+eKjvGRHqiYqJ4tidY4AsSPGuQraFaFw2bqn/T12cQlEURv8zmp92xK0QN9ljMj+1/EmKqQxgervp1CxZk1cRr/BY4MGbyDefdJ2Z+2dyze8auaxyMa3ttBROKdIjKahEhhEVE8Wea3vov6o/+b/Jj+s0V2YfmM2jF4/IYpaFNhXbsKbPGp7PfM7uYbvp59pPdrX/gPaV21PApgD+of5ptllmRjZr/ywGrhkIwND6Q1ncbTHGRsYqp0o6SzNLulbrCqTNXj/iw84+OMubyDfkssqFcwFnteMYlM9Z6l+r1TL478HM2Be3yumcjnNkbkwGYmZixoYBG8ibPS/X/K7Rf1X/ZO+5eD/wPpN2TAJgRrsZ5LTKmRpRRTojBZVI195EvmHThU10WdIFu5F2NJnThCXHl/D89XNsstjQrVo3Ng/aTODMQP756h+6VOticKuoGSozEzP98AjZ6PfTKYrCLzt+YeSGuB6/MY3HMKfjnHQ5D0O3eermS5sJeh2kcprM7d3hfunxZyk1NXNuRp5seXj++jk7vHck+XkxsTH0XtGbBUcWoNFoWNJ9CV+7fZ2KSYUa8tvkZ0P/DRgbGfP32b/549AfSX6uoigM+msQEVER1HesT7fq3VIxqUhP5K+wSHdC3oSw6tQqWv/ZmlwjctF2YVv+Pvs3ryJekTd7XgbWGci+Eft4PuM5q/qsolWFVgY94d+Q9XftT1bzrFz1u6p/AyeSTlEUxm0ex/it4wH4ueXPTPaYnG6HDpUvXJ5KRSoRHRvN6tOr1Y6Tqcn+U+8Xb6n/JPamRsdE08WzCyv/XYmxkTFr+qyhb+2+qZhSqKm2fW2mt5sOwMiNI5O8kf36c+vZe30v5ibmLOiyIN3+LRcpTwoqkS48e/mMhUcW0nBWQ3KPyk2P5T3YcnkLb6PfUixXMUY1HMXJMSfxm+rHgq4LaFC6QbqZm2LIcmTNQe+avQHZ6De5tFotw9cP5/fdvwNxQ0N+aP5Duv8HWDecyvO4p/RaquRN5BtO3z8NyIIU76Nb7W/P9T34Bvt+8Ny30W9ps7ANG85vwNTYlI0DNtL5i85pEVOoaJjbMDpU6UBMbAztFrXj2ctnHzw/5E0Iw9cPB+D7Zt9jn9c+DVKK9EIKKmGw7gfeZ8a+GdT8vSYFvi3AV399xf4b+4mJjaFsgbL82PxHLo2/xL3f7jG93XRqlKwhQ19SwXD34Wg0GvZc28N1v+tqx0kXYrWxDFgzgLkH5wIwv8t8RjYcqXKqlNGpaieymGXhxrMb+jf1Im2duHOC6NhoCtsWpoRdCbXjGKRSeUpRx74OiqKw/OTy9573JvINLf5owfYr27EwtWDr4K20rtg6DZMKtWg0Gjy7e1Imfxn8Q/3psLjDB+fcfef1HQGvAnDK58S3jb5Nw6QiPZB3n8JgKIrCNb9r/LT9Jyr8VIES40rwzcZvOHXvFIqi8EWxL/jd43du/XyLqxOvMqnlJMoXLp/uP/E3dMXtitO6QtwbjFkHZqmcxvDFxMbQfWl3PI97YqQxYkWvFXxV9yu1Y6WYbJbZaF+5PSCLU6jl3eF+8vfv/d5d6j9WG5vg8VcRr2g8uzH7b+wnq3lWdn29iybOTdI6plCRlYUVXl95kc0yG8fvHOfbTYkXSifvnmTxscUALOy6MF1sdSHSlhRUQlVarZYz988w5p8xOPzggPNEZyZsm8Bl38sYGxlTz6Ee8zrNw3eKL6fHnWZMkzHSza6CUQ1GAbDm9BoCXgWonMZwRUZH0n5Re/4++zcmxias67+OHjV6qB0rxenmlqw/t55XEa9UTpP56OYzujnK/KkP8ajogU0WGx4HP9YXoTrBb4Jxn+nOibsnyG6Znf0j9lPPsZ5KSYWa7PPas6r3KgBmH5jNurPxV7WNioliwOoBQNxQUld71zTPKAyf6gVVZGQk48aNo3LlytSqVYtly5Ylel63bt1wcHBI8N/YsWP156xYsYLatWtToUIFxo0bR0RERFq9DJEMMbExHPY5zNC/h1J4TGGqTa7G1L1TufP8DuYm5jR3ac6ynsvwn+7PoW8OMaT+EAraFlQ7dqZWvUR1vij2BZExkcw/PF/tOAYpIiqCVvNbsfnSZsxMzPD6yot2ldupHStV1ChRA6d8ToRHhcuS+mks+E0wl3wvATJ/6mMszSzp+kXcUv/v7kn1/NVz6k2vx7mH58hplZNDow5RvUR1tWIKA9CyfEvGNol7P9lnZR+u+V3TPzZ973SuP72OnbUdU9tOVSuiMHCqF1RTp07l2rVrrFy5kgkTJvDHH3+wZ8+eBOfNmzePEydO6P/7888/MTU1pXPnuImje/fu5Y8//uCnn35i5cqVXLlyhWnTZLM1Q/E2+i07ruyg94re5P0mL/Vn1OePw3/g99IPK3MrOlTpwPr+6wmcFcj2odvpVbMXuaxzqR1b/D+NRsOohnG9VPOPzCciSj6seFfY2zCazW3Gnmt7sDSzZMfQHXxZ7ku1Y6UajUajX0Ld87inymkylyO3jqAoCk75nMhnk0/tOAZP15u67co2giOC8Qvxo860Ong/8SZPtjwc+eYIFYtUVDmlMAQ/t/oZdyd3wqPC8ZjvQWh4KHef3+XnnT8DMKv9LGyz2qqcUhgqEzVvHh4ezsaNG1myZAllypShTJky3Llzh7/++ovGjRvHO9fGxkb//2NjY5k1axZ9+/bF2TluQ8NVq1bRo0cP6tWL67KfNGkSffr0YfTo0VhaWqbZaxL/8/rta3Zd3YXXRS92Xd1FWGSY/rGcVjlpWa4lHhU9cHNyw8LUQsWkIilaV2hNkZxFePTiEatPr6a/a3+1IxmEl+EvaTq3Kf/e+xdrC2t2Dt1JbfvaasdKdd2qd+M7r+849/AcV3yvUK5QObUjZQoy3C95yhUqR+UilTn/6DzLLizj7M6z3A+6TyHbQhwceZBSeUqpHVEYCGMjY9b2W0ulXypx5/kdeizvQXhUOG+j3+Lu5C4rP4oPUrWHysfHh5iYGCpUqKA/VqlSJa5cuYJWq33v87y8vAgNDaVfv7gJp7GxsVy9epXKlSvrzylfvjzR0dH4+Pik3gsQCUTFRLH85HKaz21OrhG56Li4IxvObyAsMowCNgUYWn8oh0Ydwn+6P0t7LqWZSzMpptIJE2MThrsPB2DW/lkf/B3NLIJeB1F/Rn3+vfcvObLk4ODIg5mimAKws7ajVflWgPRSpSXZfyr5+rnGvVdYd20d94PuU9yuOMe/PS7FlEggl3Uu/hn4D2YmZmy9vJX9N/ZjYWrBgq6y55T4MFV7qAIDA8mRIwdmZmb6Y7ly5SIyMpKXL19ia5uwa1VRFDw9PenevTtZs2YF4NWrV0RGRpI7d279eSYmJtjY2ODv75+sTLGxCVcCUoMuh6HkSapfd/7KTzt+0n9dKncpWlVoRevyralcpHK8Zc0N7bWl1zZPSz2r92TC1gn4+Puww3sHzZybffY102u7Pwt9RuM5jfVj6/cO24tLQZd08zpSot171ezFxgsbWXNmDZNbT8bSTEYDfMzntLtfiB+3/G9hpDGidsna6eZnTW3tKrZjxPoRhEeF45DHgX3D91HApoC0XxpIj3/fKxauyLyO8xiwJm4hiu+bfk+xnMXS1WtIj+1uiJLTfqoWVBEREfGKKUD/dVRUVKLPOXPmDP7+/rRv315/7O3bt/Ge++613ned97l69Wqyzk9thpbnYw55HwKgSakm9KzQk+I5isd9qhMK3t7eKqdLmvTW5mmtpUNLVl9ZzS9bfqFAbIEUu256anf/MH8GbR/E49DH2GWxY0HTBWiDtFwOuqx2tGT7nHbPpeQin1U+noU9Y/aW2TSxlyWnk+pT2n3HrR0AOOZy5OHthymcKGP7psY3nPM7x4gaIwh8FEjgo0C1I2Uq6envO0Alq0oMrjqYZ2HPcM/tzuXLl9WO9EnSW7unZ6oWVObm5gkKHt3XFhaJDwPbu3cvrq6u8eZUmZubx3vuu9dK7vwpZ2dnjI2Nk/Wc1KAbxmgoeZLq2aa4ncZHNR9FXYe66oZJpvTa5mntp8I/8ffVvznndw5yQvlC5T/reumt3e8F3qPNrDY8Dn1M0ZxF2Td8H8XtiqsdK9lSqt0HPB3AxO0TOfDkAGPbj/34EzK5z2n3uZfjNopuVrEZ5cuXT4V0GZezs3O6+juTUaS3v+/vmlN+jtoRPll6bndDomvHpFC1oMqTJw8hISHExMRgYhIXJTAwEAsLC7Jly5boc44fP86QIUPiHbOxscHc3JygoCBKlIjbNT4mJoaXL19iZ2eXrEzGxsYG9cNnaHk+5G30Wx4EPQCgTIEy6Sb3f6WnNldDUbuitK/cnrVn1zL74GxW91mdItdND+1+89lN3Ga48Sz0GfZ57Dkw8gCFbAupHeuzfG67967Vm592/MTR20e5H3Rf5qUkUXLbXVEUDt2KGwHQoHQDg/9dMVTp4e9MRiTtrg5p97Sj6qIUTk5OmJiYxOtKvXDhAs7OzvHm2ugEBwfj6+tLpUqV4h03MjLC2dmZCxcu6I9dvnwZExMTHB0dUy2/iO/u87toFS3ZLbOTJ1seteOIVDSywUgA1p1bh1+In8pp0sYV3yvUmVaHZ6HPKFugLEdHH033xVRKKGRbiMZl41ZlfXevH5Gy7gTc4UnIE8xMzKhZoqbacYQQQrxD1YLK0tKSVq1aMXHiRLy9vTlw4ADLli2je/fuQFxvlW5+FMCdO3cwNzenYMGEm7x27tyZpUuXcuDAAby9vZk4cSLt27eXJdPTkI9/3IqKjnkdZTWcDK5y0cq42rsSExvDvEPz1I6T6s4+OEvd6XUJfB1IxcIVOfLNEfJmz6t2LIOh25NqxakVRMdEq5wmY9Kt7lejRA2ymGdROY0QQoh3qb6x79ixYylTpgw9evRg0qRJDB06lIYNGwJQq1Ytdu3apT/3xYsXZMuWLdE3682aNWPAgAH8+OOP9O7dGxcXF0aPHp1mr0PEDYeCuIJKZHyjGsRt9Lvo2CLC3oZ95Oz06/jt47jPdOdl+Euql6jOwVEHyWmVU+1YBqW5S3PyZMtDwKsAdl7dqXacDEn2nxJCCMOl6hwqiOulmjJlClOmTEnw2K1bt+J93bRpU5o2bfrea/Xv35/+/WWzUbX4PPv/Hqp8UlBlBs1dmlMqdynuPL/D8pPLGeo2VO1IKW7/jf20/LMlEVER1HOox7Yh27CysFI7lsExNTGlZ42eTNkzBc/jnrSq0ErtSBmKVqvl8K3DANR3rK9yGiGEEP+leg+VyDjeHfInMj4jIyNGNBgBwOyDs4nVZqz9LrZd3kbzec2JiIqgqXNTdn69U4qpD+hTqw8Au6/t5knwE5XTZCxXnlwh+E0wVuZWVClaRe04Qggh/kMKKpEitFqtFFSZUI/qPbDNasv9wPtsvbxV7TgpZv259bRZ2IaomCg8KnqwedBm2bT2I0rlKUUd+zpoFS3LTy1XO06GohvuV8e+DqYmpiqnEUII8V9SUIkU4ffSj/CocEyMTShhV0LtOCKNZDHPwld1vgJg5v6ZKqdJGStPraTzks7ExMbQ5YsurO+/HjMTs48/UdC3dtziFEtPLEWr1aqcJuPQLUjh5iTzp4QQwhAlu6AKCAhIjRwindP1TpWwKyGfoGYyg+sNxszEjJN3T3Lm/hm143yW+Yfn03N5T7SKln61+7Gy90pMjFWfapputKnYBpssNjx68UhfBIjPExUTxbHbxwApqIQQwlAlu6CqV68effv2ZdeuXURFRaVGJpEO6RakcMrrpHISkdby2eSjc9XOQPrupZq+dzqD/x4MwDC3YSzqtghjI9kQMTkszSzp+kVXADyPe6qcJmM4++As4VHh5LLKRdn8ZdWOI4QQIhHJLqgmT56MVqvlm2++oVatWkyaNImrV6+mRjaRjujnT8kKf5mSbnGKfy78w8Ogh+qGSSZFUfhp+0+M/idum4VxTccxq8Ms2UvtE+mG/W2+tJmg10Eqp0n/dPOn6jvWT3TDeyGEEOpL9l/nli1bsmzZMg4fPkzv3r05ffo07dq1o3nz5ixbtoygIPkHNDOSBSkyN5eCLjQo3QCtomXuwblqx0kyRVH4btN3TNg2AYBfWv3Cr61/lWLqM5QrVI7KRSoTHRvNqn9XqR0n3ZP5U0IIYfg++eOuPHnyMHDgQHbv3s2mTZvIkSMH06ZNo27dugwdOpQrV66kZE5h4KSgErqNfj1PeBIaHqpymo/TarV8vfZrpu6dCsCsDrP4vtn3KqfKGHS9VJ4nPFEUReU06debyDecvn8akA19hRDCkH3W+IHz588zfvx4+vTpw4ULF6hZsybfffcdERERdOrUiRUrVqRQTGHIXkW84unLpwA45HVQOY1QS8MyDSmTvwyv377G84Rhz5+J1cbSd1Vf/jj8BxqNhkXdFjHcfbjasTKMTlU7kcUsCzef3eTfe/+qHSfdOn7nONGx0RTJWYTidsXVjiOEEOI9kl1QPXr0iLlz5+Lu7k63bt34999/6datGwcPHsTT05OuXbvi6elJ06ZNWbBgQWpkFgbmlv8tAPJmz4tNFht1wwjVaDQaRjYYCcCcg3OIjolWOVHiomOi6erZleUnl2OkMWJV71X0d+2vdqwMJZtlNjpU6QBg8MW1ITvkcwiImz8lw1CFEMJwJbugatSoEUuXLqVcuXIsW7aMAwcOMHjwYPLlyxfvvOLFi1OoUKEUCyoMlwz3Ezqdv+hMbuvc+Ab78s+Ff9SOk0BkdCTtFrVj3bl1mBibsH7AerpW66p2rAypb624YX/rz63nVcQrldOkT7oFKWS4nxBCGLZkF1Tjx4/nxIkTzJgxg+rVq7/3vEGDBvHPP4b3hkqkvJvPbgJSUAmwMLVgSP0hQNwS6oY0fyY8MpyWf7Zk6+WtmJuYs2XQFtpWaqt2rAyreonqOOVzIjwqnLVn16odJ90JfhPMJd9LQFwPlRBCCMOV7IKqS5cuHD9+nB9//FF/7OLFi7Rt25ZDhw6laDiRPkgPlXjXwDoDsTC14Pyj8xy/c1ztOAC8fvuapnObsvf6XrKYZWHn1ztp5tJM7VgZmkaj0fdSyZ5UyXfY5zCKolA6X2ny2eT7+BOEEEKoJtkF1ZYtWxg5ciQvX77UH7OxscHOzo4hQ4Zw4MCBlMwn0gEpqMS77Kzt6FG9B2AYG/2GvAmhwcwGHL19lGyW2dg3Yp8sQZ1GulXvhqmxKecfnefy48tqx0lXZLl0IYRIP5JdUC1dupRevXoxd+7/9popXrw4CxYsoEePHsyfPz9FAwrDFh0Tzd3ndwHZ1Ff8j27FvG1XtnEn4I5qOQJfB1J/Rn3OPDiDbVZbDo48SM2SNVXLk9nYWdvRukJrAJaeWKpymvRFtyCFFFRCCGH4kl1QPX78mDp16iT6mKurK/fv3//sUCL9eBD0gOjYaLKYZaFQDlmERMRxzOdIc5fmKIrC7AOzVcnw7OUz6k6ry2Xfy+S2zs2Rb45QuWhlVbJkZro9qdacWUNEVITKadIHvxA/bvnfwkhjRB37xP+9FUIIYTiSXVDZ2dnh7e2d6GM+Pj7kyJHjs0OJ9EM33M8hrwNGRp+1rZnIYHRLqC8/tZwXYS/S9N6PXjyi9tTa3Hh2gwI2BTj27TGcCzqnaQYRx83RjSI5i/Ay/CWbLm5SO066oFvdr1KRSrIVhRBCpAPJfgfcvHlzFixYwJo1awgICCA6OpqAgADWrVvHvHnzaNGiRWrkFAZK5k+J96nrUJcKhSsQERXBoqOL0uy+d5/fpfbU2twLvEexXMU4/u1x2XBaRUZGRvSp1QeQxSmSSuZPCSFE+pLsgmrw4MHUrl2bX375hbp16+Li4kLdunWZOHEirq6uDB06NDVyCgMlBZV4n3c3+p13eB6R0ZGpfs8bT2/gOtUV32Bf7PPYc2z0MYrZFUv1+4oP61WjF0YaI47ePspt/9tqxzFoiqLI/lNCCJHOmCT3CaampsydO5fbt29z4cIFQkNDsba2plKlSjg6ypvqzEYKKvEh7Su357tN3+H30o9159bRo0aPVLvXpceXaDirIUFhQTgXcGb/yP3kyZYn1e4nkq6gbUGalG3Czqs7WXZyGb+3+V3tSAbrTsAd/F76YWZiJguoCCFEOvHJk17s7e3p1KkTAwcOpEuXLvpiKiwsLMXCCcOmKAo+z/6/oJIV/kQizEzMGFo/rtd6xr4ZqbbR7+l7p6k/oz5BYUFULlKZw98clmLKwOgWp1hxagXRMdEqpzFcuuF+NUrUwNLMUuU0QgghkiLZPVRRUVGsXLmSs2fPEhUVpX+DpCgK4eHh3L17lytXrqR4UGF4Al8HEhIegkajoVTuUmrHEQaqv2t/ft75M1f9rnLw5kHcS7un6PWP3jpK83nNCYsMo2bJmuwcupPsWbKn6D3E52vm3Iw82fIQ8CqAHd47aF2xtdqRDJIM9xNCiPQn2T1UU6dOZcaMGQQEBHDv3j38/PyIiIjA29ubmzdvMmDAgNTIKQyQbrhf0ZxF5ZNU8V45suagd83eAMzYPyNFr7332l4az2lMWGQYbk5u7B2+V4opA2VqYkrPGj0B8Dwhi1MkRqvVcvjWYUAWpBBCiPQk2QXVvn376NWrF9u2baNr166ULVuWjRs3sm/fPgoUKIBWq02NnMIA3Xx2E5D5U+LjhrsPx0hjxJ5re7judz1Frrn18lZa/NmCt9FvaebcjB1Dd5DVPGuKXFukDt1qf3uu7cE32FflNIbnsu9lgt8EY21hTZWiVdSOI4QQIomSXVAFBwfj6uoKxM2junr1KgB58uShf//+7Nq1K2UTCoMlC1KIpCpuV5zWFeKGeM06MOuzr7f2zFraLGhDVEwUbSu1xWuQFxamFp99XZG6SuUpRV2HumgVLStOrVA7jsE55HMIgDr2dTAxTvaIfCGEECpJdkFlbW1NVFQUAEWKFOHZs2f6hSiKFi3Ks2fPUjahMFj6gkoWpBBJoFtCfc3pNQS8Cvjk6yw7sYwuS7sQq42lW7VurO23FjMTs5SKKVJZ31pxi1MsPbFURjT8h25BivqO9VVOIoQQIjmSXVBVrlyZ1atXExERQZEiRbC0tOTAgQMAXLp0CSsrqxQPKQyTfoU/6aESSVCjZA2qFa9GZEwk8w/P/6Rr/HHoD/qs7IOiKAxwHcCKXivkk/x0xqOiBzZZbHj04hEHbh5QO47BiIqJ4tjtY4DMnxJCiPTmkzb2vXz5Mv3798fExITOnTszfvx4PDw8mDNnDo0aNUqNnMLAhEeG8yj4ESAFlUg6XS/V/CPziYiKSNZzp+6ZytC1cUuwj3AfwYKuCzAy+uSdH4RKLM0s6fpFVwA8j8viFDpn7p8hPCocO2s7yuYvq3YcIYQQyZDsdyOOjo7s3r2bgQMHAjBq1CgGDx5Mrly5+Oqrr/j2229TPKQwPHee30FRFGyz2mJnbad2HJFOtK7QmqI5ixIUFsTq06uT9BxFUZi4bSJjNo0B4IdmPzCj/Qw0Gk0qJhWpSbcn1ZbLWwh8HahyGsPw7nA/+aBACCHSl2T/1R4/fjxPnz6lZs24Hdw1Gg0DBw5k8eLFDBkyBDMzmcuQGby7IIW8sRVJZWJswjD3YQDM3D/zo3NoFEXh23++ZdL2SQD81vo3fm71s/zMpXPlCpWjStEqRMdGs/rfpBXWGZ1uQQrZf0oIIdKfZBdU27Zt482bN6mRRaQjMn9KfKo+tfqQzTIbt/xvsfva7veep9VqGfz3YKbvmw7A7A6zGdt0bFrFFKlM10vlecJTv0F8ZvUm8g2n758GZEEKIYRIj5JdUFWoUIEzZ86kRhaRjsgKf+JTWVtY0792fwBm7Et8o99YbSx9VvZhwZEFaDQalnRfou/ZEhlDxyodyWKWhZvPbnLq3im146jq+J3jRMdGUyRnEYrbFVc7jhBCiGRK9vJYDg4OLF26lD179uDo6EiWLFniPa7RaPjtt99SLKAwTLIHlfgcX7t9zawDszh86zCXHl9Cw/+G8EXHRNNtWTfWn1uPsZExK3utpEu1LiqmFakhm2U2OlTpwPKTy/E87knNkjXVjqSagzfj5k+5ObrJcFYhhEiHkt1DtX//fnLnzk10dDRXr17lzJkzCf4TGZtWq+VWwC1ACirxaQrZFqJ95fYAzD44W3/8bfRb2ixsw/pz6zE1NmXDgA1STGVguj2pNpzfQGh4qMpp1KNbkEKWSxdCiPQp2T1Uhw4dSo0cIh3xDfElIioCU2NTiuUqpnYckU6NbDCStWfXsv7cerqU6kJ4VDhtFrZh/439WJhasOmrTTR1bqp2TJGKqpeoTul8pbnx7Abrzq1jQJ0BakdKcy/CXnDZ9zIg86eEECK9krVZRbLpFqQolbuUbKoqPlnlopVxtXclRhvD8kvLaTq3Kftv7CereVZ2fb1LiqlMQKPR/G9xiky6J9WRW0dQFIXS+UqTN3teteMIIYT4BMl+N9y9e/ePnrNq1apPCiPSh5v+NwFZkEJ8vlENRnHs9jE2Xt8IxM2r2f31bmqUrKFyMpFWulXrxphNYzj/6DyXH1+mfOHyakdKUzLcTwgh0r9k91ApipLgvzdv3uDt7c3du3cpXlxWKMroZMl0kVKauzSnVO5SANhmteXQqENSTGUyuaxz0bpCayBuCfXMRr8ghRRUQgiRbiW7h2r16sQ3YQwNDaVfv35SUGUCssKfSClGRkb82flPft/2OzO7zKRc4XJqRxIq6Fu7LxvOb2DN6TVMazsNSzNLtSOliSfBT7gdcBsjjRF17OuoHUcIIcQnSrE5VNmzZ6d///6sWLEipS4pDJQUVCIl1Xesz9SGUylboKzaUYRK3BzdKJqzKKERoWy6uEntOGnmkE/cIk+Vi1bGJouNumGEEEJ8shRflOLFixcpfUlhQELehBDwKgCQOVRCiJRhZGREn1p9gMy1OIVu/pSs7ieEEOlbsof8nTt3LsGx2NhY/P39mT9/PmXKlEmRYMIw3fKP23+qgE0BrC2sVU4jhMgoetboyYRtEzh6+yi3/W9jn9de7UipSlGUeBv6CiGESL+SXVB169YNjUaDoij6Hd0VRQEgX758jBs3LmUTCoOiH+4nvVNCiBRU0LYgTco2YefVnSw9sZQpbaeoHSlV3Q64jd9LP8xNzKlZsqbacYQQQnyGZBdUiS2JrtFosLKywsHBASMj2doqI5P5U0KI1NK3dl92Xt3JilMr+KXVL5iamKodKdXo5k/VKFEj0yzCIYQQGVWyq5+qVavi6OjI27dvqVq1KlWrViVfvnxcvHiRN2/epEZGYUCkoBJCpJZmzs3Imz0vz18/Z4f3DrXjpKrDtw4Dsly6EEJkBMkuqO7du0ezZs2YOHGi/pivry+TJ0+mTZs2PH36NCXzCQMjBZUQIrWYmpjSs0ZPIGPvSaVVtBy5fQSQBSmEECIjSHZBNW3aNPLkycPatWv1x6pXr87Ro0exsbFh6tSpKRpQGI7omGjuBd4DpKASQqSO3jV7A7Dn2h58g31VTpM6bgfdJvhNMNYW1lQpWkXtOEIIIT5TsguqixcvMnToUPLkyRPveM6cORk4cCCnT59OsXDCsNwLvEdMbAxZzbNSIEcBteMIITKgUnlKUdehLlpFy/KTy9WOkyrO+p0FoI59HUyMkz2VWQghhIFJdkGl0WiIiIhI9LGYmBiio6M/O5QwTDef3QTieqd0KzwKIURK61e7HwBLTywlVhurcpqUd84vbvsRmT8lhBAZQ7ILqipVqvDnn38SHBwc7/jLly9ZuHAhVatWTbFwwrDI/CkhRFrwqOhBjiw5eBz8WL9XU0YRFRPFpWeXANl/SgghMopkjzUYNWoU7du3x83NjfLly2Nra0tISAiXL1/GzMyMGTNmpEZOYQCkoBJCpAULUwu6VuvKvEPz8DzuScMyDdWOlGLOPDjD25i32FnbUSZ/GbXjCCGESAHJ7qEqVqwYO3bsoGPHjoSHh3Pt2jVevXpF+/bt2bJlC8WKFUuNnMIAyKa+Qoi00rd2XwC2XN5C4OtAldOknP039wNQz6Ge7NsohBAZxCfNhs2TJw/9+vXD1tYWgNDQUAIDA8mbN2+KhhOGQ1EU6aESQqQZl4IuVClahXMPz7Hq31WMajhK7UifRFEUrvldw+uiF5svbebKkysA1HeQ5dKFECKjSPbHY69fv6Zv37506dJFf+zKlSs0b96cr7/+mrdv36ZoQGEY/EP9eRXxCiONEaVyl1I7jhAiE9AtTuF53BNFUVROk3RarZYz988w5p8x2P9gj8skFyZun8iVJ1cwNjKmRqEatK3UVu2YQgghUkiyC6rp06dz8+ZNhg4dqj9WrVo15s2bx8WLF5k3b16KBhSGQdc7VdyuOOam5iqnEUJkBh2rdiSreVZ8/H04de+U2nE+KCY2hkM3DzHk7yEUHlOYapOrMXXvVO4+v4u5iTktyrVgRa8VPJ36lLnN5mKTxUbtyEIIIVJIsof8HTp0iDFjxtC0aVP9MTMzMxo0aMDr16+ZN28eo0ePTvL1IiMjmTRpEvv27cPCwoLevXvTu3fvRM+9desWEydO5Pr16xQpUoTvv/+eatWqAXHDDv+7wqCNjQ1nzpxJ7ksUiZDhfkKItGZtYU2Hyh1YdnIZnsc9qVmyptqR4nkb/ZYDNw7gdcmLbVe28SLshf4xawtrmjk3w6OiB03KNsHKwgqA2NhYfMmYGxYLIURmleyCKiwsjOzZsyf6mJ2dXYLl1D9m6tSpXLt2jZUrV/L06VPGjBlD/vz5ady4cbzzXr9+Te/evalfvz6///47W7duZciQIezdu5ecOXNy9+5dbGxs2LFjh/45MuE35UhBJYRQQ9/afVl2chkbzm9gdofZZM+S+L8/aeX129fsuroLr4te7Lq6i7DIMP1juaxy0bJ8SzwqeuDm6Ca9+UIIkUkku6BydHRk06ZN1KlTJ8FjW7ZswcHBIcnXCg8PZ+PGjSxZsoQyZcpQpkwZ7ty5w19//ZWgoNq8eTNZsmRh4sSJGBsb8/XXX3P06FGuXbtGnTp1uH//PsWKFcPOzi65L0kkgc8zKaiEEGmvWvFqlM5XmhvPbrD27FoG1h2Y5hmCXgex3Xs7Xhe92H9jP5ExkfrHCuYoSOsKrfGo6EGtkrUwMf6ktZ6EEEKkY8n+yz9w4EAGDhyIh4cHDRo0IGfOnAQHB3P48GGuXr3KggULknwtHx8fYmJiqFChgv5YpUqVWLhwIVqtNl4P09mzZ3Fzc8PY2Fh/bNOmTfr/f/fuXYoWLZrclyOSSJZMF0KoQaPR0M+1HyPWj8DzhGeaFVRPgp+w5fIWvC56cfT2UbSKVv9YqdylaFOpDR4VPKhctDIajSZNMgkhhDBMyS6o6tSpw/z585k3bx5z585FURQ0Gg1OTk7Mnz8/0Z6r9wkMDCRHjhyYmZnpj+XKlYvIyEhevnypX5YdwNfXFxcXF8aPH8+hQ4coUKAAY8aMoVKlSgDcu3ePmJgY2rZtS0BAAJUrV2bs2LHkzp07Wa8vNjY2WeenFl0OQ8jzJvINj4MfA1DKrpRBZEoNhtTmmYm0uzrSU7t3qtKJMZvGcOHRBc4/OE+FwhU+/qRPcCfgDpsvb2bL5S2cfXA23mMVClWgZfmWtK7QmtL5SuuLKK1Wm9il3is9tXtGIu2uDml3dUi7p4zktJ9G+Yy1aHWFj7W1NVmyZAHi5jpZW1sn6flbtmxhzpw5HD58WH/M19cXd3d3jh49Gm9fqwYNGhASEkL37t1xd3dn586d/PXXX+zevZt8+fJRv359bG1tGTt2LIqiMGvWLCIiIti4cWO8Xq33iY2N5fLly8lrgEzCJ8iHrv90xcbChgM9D6gdRwiRCY3dP5b99/bTrkw7xtQekyLXVBSFOy/ucPjBYQ49OMS94Hv6xzRocMnrQr1i9ahXrB4FshVIkXsKIYRIX8qXL//RWuKzBnubm5uTJ08eALy9vVm7di179uzh0qVLSX5+VFRUvGO6ry0sLOIdNzY2xsnJia+//hqA0qVLc/LkSbZu3crAgQPZuXMnGo1G/7y5c+dSq1Ytrly5QsWKFZP8mpydnZNUgKW22NhYrl69ahB5bp69CUCZgmUoX768qllSkyG1eWYi7a6O9Nbuo8xHsX/Ofvbd38eyAcuwNLP8pOtotVpOPzjN5kub2Xp5K/eD7usfMzEyoZ5jPVqXb02Lci3Imz3lN6tPb+2eUUi7q0PaXR3S7ilD145J8VkFVXh4ONu2bWP9+vX4+MTNsalSpUqSn58nTx5CQkKIiYnBxCQuSmBgIBYWFmTLli3euXZ2dhQvXjzesaJFi/Ls2TMALC3j/+OaM2dObGxsCAgISNZrMjY2NqgfPkPIc/v5bQCc8jmpniUtGEKbZ0bS7upIL+3eoHQDiuUqxoOgB2y+vJlu1bsl+bnRMdEcvX0Ur0tebLm0hWehz/SPWZha0LhMYzwqetDcpTk5suZIjfgJpJd2z2ik3dUh7a4Oafe080kF1c2bN1m7di07d+4kPDycIkWKMGzYMFq2bEm+fPmSfB0nJydMTEy4fPkylStXBuDChQs4OzsnWPK8fPnynDt3Lt6x+/fv07x5c8LCwqhXrx7z5s3T70sVEBBASEhIgiJMJJ+s8CeEUJuRkRF9avXhhy0/4HnC86MFVURUBPtu7MProhfbr2wnJDxE/1g2y2x86fIlHhU9aFSmEVnNs6Z2fCGEEBlYkguqyMhIduzYwfr167l69SpZs2bFzc2N7du38/PPPyerZ0rH0tKSVq1aMXHiRH777TeeP3/OsmXLmDx5MhDXW2VtbY2FhQUdO3ZkzZo1zJs3jxYtWrBlyxZ8fX1p2bIlVlZWVKpUicmTJ/Pzzz9jbGzMr7/+Su3atZO1jLtInOxBJYQwBD1r9OTHrT9y7PYxbvvfxj6vfbzHQ8ND4/aIuhS3R1R4VLj+sdzWufV7RNV3rI+Zidl/Ly+EEEJ8kiQVVL/88gvbtm0jLCyML774gilTptCwYUOioqLYtm3bZwUYO3YsEydOpEePHlhZWTF06FAaNmwIQK1atZg8eTIeHh4UKFAAT09Pfv31VxYvXkyJEiVYvHixfg7XlClT+P333+nfvz9RUVG4ubnxww8/fFY2AbHaWG4H/G/InxBCqKVAjgI0dW7KDu8dLD2xlCltp/D81XO2XdmG10UvDtw8QHRstP78wraF8ajogUcFD2qUrIGxkQx9EUIIkfKSVFCtWbMGBwcHJk6cGG/PqOjo6A88K2ksLS2ZMmUKU6ZMSfDYrVu34n1dqVIlvLy8Er1O9uzZ9T1bIuU8evGIyJhIzE3MKZKziNpxhBCZXN/afeMKqpNLOfPgDMfvHI+3R5RTPif9RrsVC1eUPaKEEEKkuiQVVAMGDGDr1q107tyZUqVK4eHhQYsWLTA1NU3tfEJluuF+9nns5dNdIYTqmpZtSt7sefEP9efo7aMAVCpSCY8KHrSu2Fp60oUQQqS5JBVUI0aMYPjw4Rw/fpxNmzYxc+ZMpk+fzhdffIFGo+EztrISBk4WpBBCGBJTE1MWdFnAilMrqOtQl9YVWkvvuRBCCFUleVEKjUaDq6srrq6uhIaGsm3bNry8vFAUha+++go3NzeaNWtGrVq1ZInGDES/IEU+KaiEEIahVYVWtKrQSu0YQgghBPCJy6Znz56dbt260a1bN27evMmmTZvYsWMH27Ztw8bGhtOnT6d0TqESWeFPCCGEEEKI9zP6+Ckf5uTkxA8//MDx48eZOXMmzs7OKZFLGAgpqIQQQgghhHi/T+qhSoypqSlNmzaladOmKXVJobIXYS8IfB0IxC1KIYQQQgghhIjvs3uoRMal650qZFsIKwsrldMIIYQQQghheKSgEu8lK/wJIYQQQgjxYVJQifeS+VNCCCGEEEJ82CfNoVIUhZs3bxIeHp7oHlRVqlT57GBCfVJQCSGEEEII8WHJLqi8vb0ZNmwY/v7+CR5TFAWNRsPNmzdTJJxQl66gcsrnpHISIYQQQgghDFOyC6rJkydjYmLC5MmTyZs3L0ZGMmowI4qMjuR+4H1AeqiEEEIIIYR4n2QXVNevX2fmzJm4u7unRh5hIO4+v4tW0ZLNMht5s+dVO44QQgghhBAGKdndSzlz5sTY2Dg1sggD8u78KY1Go3IaIYQQQgghDFOyC6rOnTuzaNEiwsPDUyOPMBCyIIUQQgghhBAfl+whf48ePeLevXvUrFmTUqVKYWFhEe9xjUbDypUrUyygUIcUVEIIIYQQQnzcJxVUjo7/e5P932XTE1tGXaQ/sqmvEEIIIYQQH5fsgmr16tWpkUMYEEVR/tdDlU8KKiGEEEIIId7nkzb2BQgNDeX8+fM8f/6cRo0a8fLlS4oVKyYLGGQAT18+JSwyDGMjY0rYlVA7jhBCCCGEEAbrkwqqBQsWsGjRIt6+fYtGo8HFxYXZs2cTEhLCsmXLyJYtW0rnFGno5rO4jZlL2JXAzMRM5TRCCCGEEEIYrmSv8rdmzRrmzZtHr1692LBhg37OVNeuXfH19WXOnDkpHlKkLVmQQgghhBBCiKRJdkG1evVq+vfvz7BhwyhTpoz+eJ06dRg+fDiHDh1K0YAi7UlBJYQQQgghRNIku6B6+vQpVatWTfSx4sWLExQU9NmhhLp0BZVTPieVkwghhBBCCGHYkl1Q5cuXj0uXLiX62LVr18iXL99nhxLqkiXThRBCCCGESJpkL0rRtm1b5s2bh4WFBXXr1gUgPDycvXv3smjRInr16pXSGUUaev32NX4v/QBwyOugchohhBBCCCEMW7ILqn79+vHkyROmT5/O9OnTAejevTsAX375JQMGDEjZhCJN3fK/BUCebHnIkTWHymmEEEIIIYQwbMkuqDQaDT/99BO9e/fm9OnTvHz5Emtra6pUqYK9vX1qZBRpSBakEEIIIYQQIumSXVD5+/uTN29eihYtStGiReM9Fhsby+LFi/nqq69SKp9IYzJ/SgghhBBCiKRL9qIUXbt25dmzZwmOe3t707p1a+bOnZsiwYQ69D1U+aSgEkIIIYQQ4mOSXVBlzZqVrl274ucXt3BBREQEv/32G506dSIsLIyFCxemeEiRdmTInxBCCCGEEEmX7IJqzZo15MqVi27durF582aaNWvG33//Ta9evdi1axd16tRJjZwiDcTExnDn+R1ACiohhBBCCCGSItkFlbW1NcuXL6dgwYKMGzeOHDly4OXlxTfffIOFhUVqZBRp5EHQA6JiorAwtaCwbWG14wghhBBCCGHwkrQoxdOnTxMcmzhxIt9++y3Pnz/n7du38c7Jnz9/yiUUaUY33M8hjwNGRsmutYUQQgghhMh0klRQ1a9fH41Gk+C4oigAdOjQId7xmzdvpkA0kdZkQQohhBBCCCGSJ0kF1W+//ZZoQSUyFt2S6U55nVROIoQQQgghRPqQpILKw8MjtXMIAyA9VEIIIYQQQiRPsjf2BQgODmbZsmWcPXuWV69ekSNHDipXrkzPnj3JmTNnSmcUaUBRFG4+ixuqKSv8CSGEEEIIkTTJXnnA39+f1q1bs3LlSszNzSldujQmJiYsX76cVq1aERAQkBo5RSoLCgsiJDwEjUZDqdyl1I4jhBBCCCFEupDsHqpp06ZhYmLCrl27KFSokP64r68vvXv3ZtasWfz+++8pGlKkPt38qSK2RchinkXlNEIIIYQQQqQPye6hOnHiBF9//XW8YgqgUKFCDB48mGPHjqVYOJF2ZP6UEEIIIYQQyZfsgio2NpYcOXIk+pitrS1hYWGfHUqkPX1BJfOnhBBCCCGESLJkF1QODg5s37490ce2bt2Kvb39Z4cSaU8KKiGEEEIIIZIv2XOoBg0aRJ8+fQgNDaVp06bY2dkRGBjIzp07OXHiBHPnzk2NnCKVSUElhBBCCCFE8iWpoOrevTsTJkygRIkS1KxZk99//53p06fHmy+VK1cufvvtNxo0aJBqYUXqeBv9lgdBDwCZQyWEEEIIIURyJKmgOnv2LG/evNF/3apVK1q2bMn9+/cJDQ0le/bsFC9eHI1Gk2pBReq5HXAbRVGwyWJDbuvcascRQgghhBAi3fikjX0BNBoNJUqUSMksQiW6JdMd8zpKUSyEEEIIIUQyJHtRCpHxyPwpIYQQQgghPk2Se6gGDx6MmZnZR8/TaDQcOHDgs0KJtKUrqJzyOamcRAghhBBCiPQlyQVV6dKlsbW1Tc0sQiXSQyWEEEIIIcSnSVYPlYuLS2pmESrQarXc8r8FSEElhBBCCCFEcskcqkzuScgTwqPCMTU2pViuYmrHEUIIIYQQIl2RgiqT0w33K5m7JKYmpiqnEUIIIYQQIn1JUkHVunVrcuTIkdpZhApk/pQQQgghhBCfLklzqCZPnpzaOYRKpKASQgghhBDi08mQv0zu3U19hRBCCCGEEMkjBVUmp++hyicFlRBCCCGEEMmlekEVGRnJuHHjqFy5MrVq1WLZsmXvPffWrVt0+r/27jysqnrf4/hnAzEoKiqIYzkFom4FUdPC69Wbw1GbPNng1cw0Oo516jqgJ4ecDmLHNLoOlUPaKTWHk00ezdLnVpaRkpSYSpkKIhooyiSw7h8e9nEnDsDerL3x/Xoentzrt/bmu76btnz8rfVbjz+udu3a6b777tOePXvsxletWqVu3bopIiJCU6ZMUW5urrPLd2vncs4p7VyaJCk0ONTkagAAAAD3Y3qgmj9/vpKSkrR69WpNnz5d8fHx+uSTT67aLzs7W0899ZRatmyprVu3qlevXho7dqzOnj0rSdq2bZvi4+P10ksvafXq1UpMTFRcXFxlH45bKZmdalCrgWpVq2VyNQAAAID7uekb+17p559/1q5du5STk6Pi4mK7MYvFojFjxtzU6+Tk5GjDhg16/fXX1aZNG7Vp00aHDx/W22+/rb59+9rtu3nzZlWrVk0zZsyQp6enxo8fr127dikpKUndu3fXW2+9pWHDhqlHjx6SpJkzZ2rEiBGaMGGC/Pz8ynOYVR4LUgAAAAAVU+ZA9Y9//EOTJ0+WYRiljpclUCUnJ6uwsFARERG2bZGRkVq6dKmKi4vl4fHvCbRvvvlG//Vf/yVPT0/bto0bN0qSioqKdODAAY0dO9Y2Fh4erkuXLik5Odnu9fFvJYEqrEGYyZUAAAAA7qnMgep///d/dffdd2v27NmqX7++LBZLub95RkaGateuLW9vb9u2wMBA5efnKysrS3Xq1LFtP378uNq1a6cXX3xRO3fuVKNGjTRp0iRFRkbq/Pnzys/PV7169f59YF5eCggI0KlTp8pUU1FRUbmPx5FK6nBmPQdTD0qSQoJDXOa4zVQZPcfV6Ls56Ls56Ls56Ls56Ls56LtjlKV/ZQ5UqampmjFjhho0aFDWp14lNzfXLkxJsj0uKCiw256Tk6Ply5friSee0Ouvv64PP/xQI0aM0Mcff3zVc698/PvXuZEDBw6UaX9nc2Y9iccSJUleF7y0f/9+p30fd+NqPwO3CvpuDvpuDvpuDvpuDvpuDvpeecocqJo1a6a0tDSHfHMfH5+rAk/JY19fX7vtnp6eCgsL0/jx4yVJrVu31hdffKF//OMfeuSRR+yee+VrlfX6KavVandaoVlKTmN0Vj2Xii7pxPITkqT+9/RXkzpNHP493I2ze47S0Xdz0Hdz0Hdz0Hdz0Hdz0HfHKOnjzShzoHrhhRc0a9YsNWrUSOHh4fLx8SlzgSWCg4OVmZmpwsJCeXldLiUjI0O+vr6qWbOm3b5BQUFq3ry53bamTZsqLS1NAQEB8vHx0ZkzZ9SiRQtJUmFhobKyshQUFFSmmjw9PV3qh89Z9RzJOKLC4kJV96mu2+vebne92q3O1X4GbhX03Rz03Rz03Rz03Rz03Rz0vfKUOVDNmTNHZ8+e1ZNPPlnquMVi0Y8//nhTrxUWFiYvr8unm3Xs2FGSlJCQIKvVetUv+OHh4dq7d6/dtpSUFA0YMEAeHh6yWq1KSEjQXXfdJUnav3+/vLy81KoVK9iVpmRBitDgUMIUAAAAUE5lDlT333+/w765n5+fHnzwQc2YMUNz587V6dOntWLFCs2bN0/S5dmqGjVqyNfXV4899pjWrl2rV199Vffff7+2bNmi48eP64EHHpAkDR48WNOmTVNISIjq1aunGTNm6JFHHmHJ9GtITmPJdAAAAKCiyhyorlya3BFiYmI0Y8YMDRs2TP7+/ho3bpx69+4tSYqKitK8efM0cOBANWrUSG+88YbmzJmj5cuXq0WLFlq+fLmCg4MlSf3799fJkyc1bdo0FRQUqHfv3powYYJDa61KbPegakCgAgAAAMqrXDf2zc/P16FDh1RQUGC7H1VxcbFyc3P17bff6n/+539u+rX8/PwUGxur2NjYq8YOHTpk9zgyMlKbNm265mtFR0crOjr6pr/3rYyb+gIAAAAVV+ZA9fXXX+vZZ5/VuXPnSh2vXr16mQIVKp9hGAQqAAAAwAHKHKgWLlyo2rVra9asWXr//ffl4eGhgQMHavfu3XrnnXf0+uuvO6NOOFD6+XRl5WTJYrHozuA7zS4HAAAAcFtlDlSHDh3S7Nmz1atXL2VnZ+vdd99V9+7d1b17d126dElLlizR8uXLnVErHKRkdqpZYDP53uZ7g70BAAAAXEuZ18suLi62LQRxxx136PDhw7axPn363PSS6TBPyQp/YfXDTK4EAAAAcG9lDlS33367bbGIZs2aKTc3VykpKZIu30z34sWLjq0QDscKfwAAAIBjlDlQ3XfffVqwYIHWrl2rOnXqqG3btpo1a5Z27typ1157TS1btnRGnXAgFqQAAAAAHKPMgWrkyJF67LHHlJiYKEmaPn26Dh48qNGjRyslJUUTJ050eJFwLAIVAAAA4BhlXpTCw8NDkyZNsj22Wq3asWOHUlJS1Lx5c/n7+zu0QDhWTn6Ojp09JolABQAAAFRUuW7sK0nnzp3Tt99+q9OnT6tPnz7y9/dX9erVHVkbnOCn9J8kSXX96yqwRqDJ1QAAAADurVyBasmSJVq2bJny8vJksVjUrl07vfLKK8rMzNSKFStUs2ZNR9cJB+F0PwAAAMBxynwN1dq1a/Xqq69q+PDhWr9+vQzDkCQNGTJEx48f16JFixxeJByHQAUAAAA4TpkD1Zo1axQdHa1nn31Wbdq0sW3v3r27nnvuOe3cudOhBcKxCFQAAACA45Q5UKWmpqpz586ljjVv3lxnzpypcFFwnpKb+hKoAAAAgIorc6Bq0KCB9u3bV+pYUlKSGjRoUOGi4BxFxUU6lH75pszc1BcAAACouDIvSvHwww/r1Vdfla+vr/7zP/9TkpSTk6Nt27Zp2bJlGj58uKNrhIP8evZX5V3Kk7eXt5rWbWp2OQAAAIDbK3Ogevrpp3XixAktWLBACxYskCQ98cQTkqT77rtPzzzzjGMrhMOUXD8VEhwiL89yr5gPAAAA4F/K/Fu1xWLRSy+9pOHDh2vPnj06d+6catSooU6dOikkJMQZNcJBWJACAAAAcKxyT1M0a9ZMzZo1c2QtcDICFQAAAOBYNxWoYmJibvoFLRaL5s6dW+6C4DwEKgAAAMCxbipQbd68WRaLRcHBwfLwuP7CgBaLxSGFwfFsS6azwh8AAADgEDcVqP7whz/o888/V0FBgfr27av+/fsrMjLS2bXBgX67+JtOZ5+WJIUGh5pcDQAAAFA13FSgWrhwoXJzc/XZZ5/po48+0vDhwxUYGKh+/fqpf//+CgsLc3adqKBDpy7ff6px7cby9/U3uRoAAACgarjpRSn8/PzUr18/9evXTxcuXND27dv10UcfadWqVWrcuLEGDBig/v37s1CFi+L6KQAAAMDxyrXKn7+/vx566CE99NBDysrK0vbt2/Xxxx9r6dKlCgkJ0aZNmxxdJyrIdv0UgQoAAABwmOuvMHET8vPzlZubq7y8PBUVFenkyZOOqAsOZpuhYkEKAAAAwGHKNUOVnp6uTz75RJ988okSExNVrVo13XvvvXrmmWd0zz33OLpGOACn/AEAAACOd9OB6soQtX//fvn5+alHjx4aOXKkunXrJm9vb2fWiQooKCzQ0YyjkghUAAAAgCPdVKB6/PHHlZiYKB8fH3Xv3l2LFi1S9+7d5ePj4+z64ABHTh9RUXGRavjWUMOAhmaXAwAAAFQZNxWo9u3bJ09PT7Vs2VK//fab1q5dq7Vr15a6r8Vi0erVqx1aJCrmytP9uPEyAAAA4Dg3Fag6depk+7NhGNfd90bjqHys8AcAAAA4x00FqjVr1ji7DjgRC1IAAAAAzlHhZdPh+lgyHQAAAHAOAlUVZxgGM1QAAACAkxCoqri0c2nKzsuWp4enWgS1MLscAAAAoEohUFVxJQtSNA9sLp/bWOYeAAAAcCQCVRXH9VMAAACA8xCoqjiunwIAAACch0BVxRGoAAAAAOchUFVxB9MOSiJQAQAAAM5AoKrCsvOydSLzhCSuoQIAAACcgUBVhf2U/pMkqV6NeqpTvY7J1QAAAABVD4GqCitZMp3ZKQAAAMA5CFRVGAtSAAAAAM5FoKrCCFQAAACAcxGoqjACFQAAAOBcBKoqqqi4yLYoBYEKAAAAcA4CVRX1y5lfVFBYIN/bfHV73dvNLgcAAACokghUVVTJ6X4hwSHy9PA0uRoAAACgaiJQVVFcPwUAAAA4H4GqiiJQAQAAAM5HoKqibDf1JVABAAAATkOgqqIOnjooSWrVgEAFAAAAOAuBqgo6k31GZy+clSSFBoeaXA0AAABQdRGoqqCS66fuqHuHqvlUM7kaAAAAoOoiUFVBLEgBAAAAVA4vswvIz8/XzJkz9c9//lO+vr566qmn9NRTT5W676hRo7Rz5067bUuXLlWPHj107tw5de7c2W4sICBAX3/9tdNqd1UEKgAAAKBymB6o5s+fr6SkJK1evVqpqamaNGmSGjZsqL59+16179GjRxUXF6euXbvattWqVUuSdOTIEQUEBOiDDz6wjXl43JoTcKzwBwAAAFQOUwNVTk6ONmzYoNdff11t2rRRmzZtdPjwYb399ttXBaqCggKdOHFCVqtVQUFBV71WSkqKmjVrVurYrcY2Q8UKfwAAAIBTmTqFk5ycrMLCQkVERNi2RUZGKjExUcXFxXb7pqSkyGKxqEmTJqW+1pEjR9S0aVNnlusW8i7l6eczP0tihgoAAABwNlNnqDIyMlS7dm15e3vbtgUGBio/P19ZWVmqU6eObXtKSor8/f01ceJEffPNN6pfv77GjRun7t27S7p8OmBhYaEefvhhpaenq2PHjoqJiVG9evXKVFNRUZFjDq6CSuooaz2H0g6p2ChWLb9aCqwe6DLH4w7K23NUDH03B303B303B303B303B313jLL0z9RAlZubaxemJNkeFxQU2G1PSUlRXl6eoqKiFB0dre3bt2vUqFFat26drFarUlJSVKdOHcXExMgwDC1cuFB/+tOftGHDBnl6et50TQcOHKj4gTlQWevZcXSHJKlJjSZKTEx0RklVnqv9DNwq6Ls56Ls56Ls56Ls56Ls56HvlMTVQ+fj4XBWcSh77+vrabR89erSGDh1qW4SiVatW+uGHH7R+/XpZrVZ9+OGHslgstuctXrxYUVFRSkxMVIcOHW66JqvVWqYA5ixFRUU6cOBAmev5MPVDSVKH5h0UHh7upOqqpvL2HBVD381B381B381B381B381B3x2jpI83w9RAFRwcrMzMTBUWFsrL63IpGRkZ8vX1Vc2aNe329fDwsIWpEs2bN9eRI0ckSX5+fnZjdevWVUBAgNLT08tUk6enp0v98JW1np/Sf5IkhTUMc6njcCeu9jNwq6Dv5qDv5qDv5qDv5qDv5qDvlcfURSnCwsLk5eWl/fv327YlJCTIarVeteT55MmTFRMTY7ctOTlZzZs314ULF9SpUyft2bPHNpaenq7MzEw1b97cqcfgag6mHZTEghQAAABAZTA1UPn5+enBBx/UjBkz9P3332vHjh1asWKFnnjiCUmXZ6vy8vIkST179tTWrVu1ZcsWHTt2TPHx8UpISNCQIUPk7++vyMhIzZs3T99//71++OEH/fnPf1a3bt0UGhpq5iFWquLiYtuS6WENwkyuBgAAAKj6TL/zbUxMjNq0aaNhw4Zp5syZGjdunHr37i1JioqK0kcffSRJ6t27t6ZPn64lS5ZowIAB2rlzp9544w01btxYkhQbG6vWrVsrOjpaQ4cOVaNGjbRgwQLTjssMJ7NOKqcgR16eXmoeeGvNzAEAAABmMPUaKunyLFVsbKxiY2OvGjt06JDd40GDBmnQoEGlvk6tWrU0b948p9ToLkpmp1oGtdRtXreZXA0AAABQ9Zk+QwXHSU67HKi4fgoAAACoHASqKqRkhqpVAwIVAAAAUBkIVFWILVAxQwUAAABUCgJVFUKgAgAAACoXgaqKOJ97XqlZqZKk0Pq3zlLxAAAAgJkIVFXEoVOXV0SsX6u+AqoFmFsMAAAAcIsgUFURnO4HAAAAVD4CVRVBoAIAAAAqH4GqiiBQAQAAAJWPQFVFHEw7KIlABQAAAFQmAlUVcKnwko6cPiJJCmsQZnI1AAAAwK2DQFUF/HzmZ10quqRq3tXUuHZjs8sBAAAAbhkEqiqg5Pqp0Pqh8vDgLQUAAAAqC799VwEsSAEAAACYg0BVBRCoAAAAAHMQqKoAAhUAAABgDgKVmzMMQ8lp/wpUDQhUAAAAQGUiULm5jOwMZeZkymKx6M56d5pdDgAAAHBLIVC5uZLT/ZrWbSo/bz+TqwEAAABuLQQqN8f1UwAAAIB5CFRuznb9FIEKAAAAqHQEKjd38NRBSSxIAQAAAJiBQOXmSmaowuqHmVwJAAAAcOshULmxnPwcHfvtmCRmqAAAAAAzEKjc2OHTh2UYhupUr6NA/0CzywEAAABuOQQqN3blCn8Wi8XkagAAAIBbD4HKjbHCHwAAAGAuApUbs81Qcf0UAAAAYAoClRvjpr4AAACAuQhUbqq4uFiH0g9JIlABAAAAZiFQuanjmceVW5Cr2zxvU7PAZmaXAwAAANySCFRuqmRBijvr3SkvTy+TqwEAAABuTQQqN8WCFAAAAID5CFRuigUpAAAAAPMRqNzUwbSDkqSwBmEmVwIAAADcughUbooZKgAAAMB8BCo3lHkxU+nn0yVJofVDTa4GAAAAuHURqNzQoVOX7z/VKKCRavjWMLkaAAAA4NZFoHJDrPAHAAAAuAYClRvi+ikAAADANRCo3BCBCgAAAHANBCo3RKACAAAAXAOBys1cKrykoxlHJRGoAAAAALMRqNzM0YyjKiwqVHWf6mpUu5HZ5QAAAAC3NAKVm7nydD+LxWJyNQAAAMCtjUDlZrh+CgAAAHAdBCo3czDtoCQprEGYyZUAAAAAIFC5GWaoAAAAANdBoHIjhmEQqAAAAAAXQqByI6fOndL53PPysHioZb2WZpcDAAAA3PIIVG6kZHaqeVBz+dzmY3I1AAAAAAhUboTT/QAAAADXQqByIwQqAAAAwLUQqNxIchqBCgAAAHAlBCo3YpuhakCgAgAAAFwBgcpNXMy/qF9/+1USM1QAAACAqzA9UOXn52vKlCnq2LGjoqKitGLFimvuO2rUKIWGhtp9ffbZZ7bxVatWqVu3boqIiNCUKVOUm5tbGYdQKX5K/0mSFOgfqLr+dU2uBgAAAIAkeZldwPz585WUlKTVq1crNTVVkyZNUsOGDdW3b9+r9j169Kji4uLUtWtX27ZatWpJkrZt26b4+HjFxcWpbt26iomJUVxcnKZNm1Zpx+JMXD8FAAAAuB5TZ6hycnK0YcMGTZ06VW3atFGvXr00cuRIvf3221ftW1BQoBMnTshqtSooKMj25e3tLUl66623NGzYMPXo0UPt2rXTzJkztXHjxiozS8X1UwAAAIDrMTVQJScnq7CwUBEREbZtkZGRSkxMVHFxsd2+KSkpslgsatKkyVWvU1RUpAMHDqhjx462beHh4bp06ZKSk5OddwCV6GDaQUlSWP0wkysBAAAAUMLUU/4yMjJUu3Zt2yyTJAUGBio/P19ZWVmqU6eObXtKSor8/f01ceJEffPNN6pfv77GjRun7t276/z588rPz1e9evVs+3t5eSkgIECnTp0qU01FRUUVPzAHKKmj5L8lM1R31rvTZWqsan7fc1QO+m4O+m4O+m4O+m4O+m4O+u4YZemfqYEqNzfXLkxJsj0uKCiw256SkqK8vDxFRUUpOjpa27dv16hRo7Ru3ToFBgbaPffK1/r969zIgQMHynoYTnXgwAEVFRfp0KlDkqTizGLt37/f3KKqOFf7GbhV0Hdz0Hdz0Hdz0Hdz0Hdz0PfKY2qg8vHxuSrwlDz29fW12z569GgNHTrUtghFq1at9MMPP2j9+vX685//bPfcK1/Lz8+vTDVZrVZ5enqW6TnOUHIao9Vq1bHfjqmgqEA+Xj76Q9Qf5Olhfn1V0ZU9d4WfgVsFfTcHfTcHfTcHfTcHfTcHfXeMkj7eDFMDVXBwsDIzM1VYWCgvr8ulZGRkyNfXVzVr1rTb18PDwxamSjRv3lxHjhxRQECAfHx8dObMGbVo0UKSVFhYqKysLAUFBZWpJk9PT5f64fP09NThjMOSpJDgEHnf5n2DZ6CiXO1n4FZB381B381B381B381B381B3yuPqYtShIWFycvLy+4UtoSEBFmtVnl42Jc2efJkxcTE2G1LTk5W8+bN5eHhIavVqoSEBNvY/v375eXlpVat3H9VPJZMBwAAAFyTqYHKz89PDz74oGbMmKHvv/9eO3bs0IoVK/TEE09IujxblZeXJ0nq2bOntm7dqi1btujYsWOKj49XQkKChgwZIkkaPHiw3nzzTe3YsUPff/+9ZsyYoUceeaTMp/y5IpZMBwAAAFyT6Tf2jYmJ0YwZMzRs2DD5+/tr3Lhx6t27tyQpKipK8+bN08CBA9W7d29Nnz5dS5YsUWpqqu6880698cYbaty4sSSpf//+OnnypKZNm6aCggL17t1bEyZMMPPQHMYWqJihAgAAAFyK6YHKz89PsbGxio2NvWrs0KFDdo8HDRqkQYMGXfO1oqOjFR0d7fAazUagAgAAAFyTqaf84cbOXjirjOwMSZcXpQAAAADgOghULu5Q+uVZuiZ1msjf19/kagAAAABciUDl4kpu6MvpfgAAAIDrIVC5uIOnDkqSwhqEmVwJAAAAgN8jULk4ZqgAAAAA10WgcnEEKgAAAMB1EahcWEFRgVLOpEgiUAEAAACuiEDlwo6fO65io1g1/Wqqfq36ZpcDAAAA4HcIVC7sl6xfJF2enbJYLOYWAwAAAOAqBCoX9kvmL5I43Q8AAABwVQQqF3blDBUAAAAA10OgcmHHso5JIlABAAAAropA5aIMw/j3DFUDAhUAAADgighULio1K1U5l3Lk6eGpFkEtzC4HAAAAQCkIVC4qOT1ZktQiqIW8vbxNrgYAAABAaQhULurQqUOSuH4KAAAAcGUEKheVnHZ5hiq0fqjJlQAAAAC4FgKViyo55a9VMDNUAAAAgKsiULmoklP+mKECAAAAXBeBygVl52XrZNZJSVJoMIEKAAAAcFUEKhdUMjtV16+ualevbXI1AAAAAK6FQOWCkk9dvn7qjoA7TK4EAAAAwPUQqFzQr2d/lSQ1rd3U3EIAAAAAXJeX2QXgag9GPKh9v+7ToJaDzC4FAAAAwHUwQ+WCWjdsrXej31WLOi3MLgUAAADAdRCoAAAAAKCcCFQAAAAAUE4EKgAAAAAoJwIVAAAAAJQTgQoAAAAAyolABQAAAADlRKACAAAAgHIiUAEAAABAORGoAAAAAKCcCFQAAAAAUE4EKgAAAAAoJwIVAAAAAJQTgQoAAAAAyolABQAAAADlRKACAAAAgHIiUAEAAABAORGoAAAAAKCcCFQAAAAAUE5eZhfgKgzDkCQVFRWZXMllJXW4Sj23AnpuDvpuDvpuDvpuDvpuDvpuDvruGCX9K8kI12MxbmavW0BBQYEOHDhgdhkAAAAAXITVapW3t/d19yFQ/UtxcbEKCwvl4eEhi8VidjkAAAAATGIYhoqLi+Xl5SUPj+tfJUWgAgAAAIByYlEKAAAAACgnAhUAAAAAlBOBCgAAAADKiUAFAAAAAOVEoAIAAACAciJQAQAAAEA5EagAAAAAoJwIVC4mPz9fU6ZMUceOHRUVFaUVK1aYXVKVkJ6ervHjx6tz587q1q2b5s2bp/z8fEnS8ePH9eSTTyo8PFz9+vXT//3f/9k998svv9SAAQPUvn17PfHEEzp+/LgZh+D2oqOjNXnyZNvjH3/8UYMGDVL79u31xz/+UUlJSXb7f/DBB7r33nvVvn17jRkzRr/99ltll+y2CgoKNHPmTHXq1El33323/va3v6nkloP03XnS0tL0zDPPqEOHDurZs6dWrVplG6PvjldQUKABAwbo66+/tm2r6Of5qlWr1K1bN0VERGjKlCnKzc2tlGNxJ6X1ff/+/XrssccUERGhPn36aMOGDXbPoe8VV1rfS2RnZ6tbt27atGmT3fbrfa4YhqEFCxaoS5cu6ty5s+bPn6/i4mKnH0dVRaByMfPnz1dSUpJWr16t6dOnKz4+Xp988onZZbk1wzA0fvx45ebm6u2339bChQv12Wef6ZVXXpFhGBozZowCAwO1ceNGPfDAAxo7dqxSU1MlSampqRozZowGDhyo9957T3Xq1NHo0aPF/bDL5sMPP9SuXbtsj3NychQdHa2OHTtq06ZNioiI0DPPPKOcnBxJ0vfff6+pU6dq7NixWrdunc6fP6+YmBizync7s2fP1pdffqk333xTL7/8stavX69169bRdyd77rnnVK1aNW3atElTpkzRK6+8ou3bt9N3J8jPz9fzzz+vw4cP27ZV9PN827Ztio+P10svvaTVq1crMTFRcXFxphyfqyqt7xkZGXr66afVuXNnbd68WePHj9esWbP0+eefS6LvjlBa368UFxen06dP22270efKypUr9cEHHyg+Pl6LFy/W1q1btXLlSqceR5VmwGVcvHjRsFqtxp49e2zbXnvtNWPIkCEmVuX+jhw5YoSEhBgZGRm2bVu3bjWioqKML7/80ggPDzcuXrxoGxs2bJixePFiwzAM45VXXrHrf05OjhEREWH3HuH6MjMzjf/4j/8w/vjHPxqTJk0yDMMwNmzYYPTs2dMoLi42DMMwiouLjV69ehkbN240DMMwJkyYYNvXMAwjNTXVCA0NNX799dfKPwA3k5mZabRu3dr4+uuvbduWLVtmTJ48mb47UVZWlhESEmIcOnTItm3s2LHGzJkz6buDHT582Lj//vuN++67zwgJCbF9Hlf083zw4MG2fQ3DMPbu3Wu0a9fOyMnJqYzDcnnX6vvf//53o2/fvnb7vvjii8bzzz9vGAZ9r6hr9b3E3r17jV69ehn33HOP7TPFMG78udK9e3e7/bds2WL06NHDyUdTdTFD5UKSk5NVWFioiIgI27bIyEglJiYyDVsBQUFBeuONNxQYGGi3/cKFC0pMTFTr1q1VrVo12/bIyEjt379fkpSYmKiOHTvaxvz8/NSmTRvbOG4sNjZWDzzwgFq2bGnblpiYqMjISFksFkmSxWJRhw4drtn3Bg0aqGHDhkpMTKzU2t1RQkKC/P391blzZ9u26OhozZs3j747ka+vr/z8/LRp0yZdunRJKSkp+u677xQWFkbfHeybb77RXXfdpXXr1tltr8jneVFRkQ4cOGA3Hh4erkuXLik5Odm5B+QmrtX3ktPof+/ChQuS6HtFXavv0uXTAF988UVNmzZN3t7edmPX+1xJT09XWlqaOnXqZBuPjIzUyZMnr5rpws3xMrsA/FtGRoZq165t9z9FYGCg8vPzlZWVpTp16phYnfuqWbOmunXrZntcXFystWvXqkuXLsrIyFC9evXs9q9bt65OnTolSTccx/V99dVX+vbbb7V161bNmDHDtj0jI8MuYEmX+1pyOsPp06fpezkdP35cjRo10pYtW7R06VJdunRJAwcO1KhRo+i7E/n4+GjatGmaNWuW3nrrLRUVFWngwIEaNGiQPv30U/ruQIMHDy51e0U+z8+fP6/8/Hy7cS8vLwUEBPA+/Mu1+t64cWM1btzY9vjs2bP68MMPNW7cOEn0vaKu1XdJWrp0qVq3bq2oqKirxq73uZKRkSFJduMl/+h86tSpq56HGyNQuZDc3Nyr/oWh5HFBQYEZJVVJcXFx+vHHH/Xee+9p1apVpfa8pN/Xek94P24sPz9f06dP17Rp0+Tr62s3dqO+5uXl0fdyysnJ0bFjx/Tuu+9q3rx5ysjI0LRp0+Tn50ffnezo0aPq0aOHhg8frsOHD2vWrFnq2rUrfa8kN+rz9cbz8vJsj6/1fNxYXl6exo0bp8DAQD366KOS6LuzHDlyRO+++67ef//9Usev97lSWt/5fbNiCFQuxMfH56of5JLHv/+FFOUTFxen1atXa+HChQoJCZGPj4+ysrLs9ikoKLD1+1rvSc2aNSurZLcVHx+vtm3b2s0OlrhWX2/Udz8/P+cVXEV4eXnpwoULevnll9WoUSNJly8Kf+edd3THHXfQdyf56quv9N5772nXrl3y9fWV1WpVenq6lixZoiZNmtD3SlCRz3MfHx/b49+P8z7cnIsXL2r06NH65Zdf9Pe//93WN/rueIZh6C9/+YvGjx9/1eUMJa73uXJlePr9e0Dfy4drqFxIcHCwMjMzVVhYaNuWkZEhX19ffoF3gFmzZmnlypWKi4tTnz59JF3u+ZkzZ+z2O3PmjG26+1rjQUFBlVO0G/vwww+1Y8cORUREKCIiQlu3btXWrVsVERFB350oKChIPj4+tjAlSc2aNVNaWhp9d6KkpCTdcccddv/41bp1a6WmptL3SlKRPgcEBMjHx8duvLCwUFlZWbwPN+HChQsaMWKEDh8+rNWrV6tp06a2MfrueKmpqdq3b59iY2Ntf8empqZq+vTpGjlypKTr9z04OFiSbKf+Xfln+l4+BCoXEhYWJi8vL7sFDxISEmS1WuXhwVtVEfHx8Xr33Xf1t7/9Tf3797dtb9++vX744Qfb9Ld0ueft27e3jSckJNjGcnNz9eOPP9rGcW1r1qzR1q1btWXLFm3ZskU9e/ZUz549tWXLFrVv31779u2zLZtrGIa+++67a/Y9LS1NaWlp9P0mtG/fXvn5+fr5559t21JSUtSoUSP67kT16tXTsWPH7P5FOCUlRY0bN6bvlaQin+ceHh6yWq124/v375eXl5datWpVeQfhhoqLizV27FidOHFCa9as0Z133mk3Tt8dLzg4WP/85z9tf79u2bJF9erV0/jx4zVnzhxJ1/9cCQ4OVsOGDe3GExIS1LBhQ66fKi8TVxhEKV588UWjf//+RmJiorF9+3ajQ4cOxrZt28wuy60dOXLECAsLMxYuXGicPn3a7quwsNDo16+f8dxzzxk//fSTsWzZMiM8PNw4efKkYRiGcfz4ccNqtRrLli0zfvrpJ+PZZ5817rvvPtvyx7h5kyZNsi3hmp2dbXTp0sWYNWuWcfjwYWPWrFnGPffcY1vu+LvvvjPatGljrF+/3jh48KAxZMgQ45lnnjGzfLcSHR1tPProo8bBgweN3bt3G126dDFWr15N353o/Pnzxj333GNMmDDBSElJMT799FOjc+fOxjvvvEPfnejKZaQr+nn+wQcfGB06dDC2b99uJCYmGv379zdmzZpl2rG5siv7vm7dOqNVq1bGZ599Zvf3a2ZmpmEY9N2RSls2vUSPHj3slkG/0efKsmXLjKioKGPPnj3Gnj17jKioKGPFihVOP4aqikDlYnJycoyJEyca4eHhRlRUlLFy5UqzS3J7y5YtM0JCQkr9MgzD+OWXX4z//u//Ntq2bWv079/f+OKLL+ye//nnnxu9e/c22rVrZwwbNox7w5TTlYHKMAwjMTHRePDBBw2r1Wo8/PDDxg8//GC3/8aNG43u3bsb4eHhxpgxY4zffvutskt2W+fPnzcmTJhghIeHG127djVeffVV2y8v9N15Dh8+bDz55JNGhw4djHvvvddYuXIlfXey3/+CWdHP82XLlhldu3Y1IiMjjZiYGCMvL69SjsPdXNn3p556qtS/X6+89xR9d4yyBCrDuP7nSmFhoTF37lyjY8eOxl133WXExcXxj8UVYDGMf52DAAAAAAAoEy7MAQAAAIByIlABAAAAQDkRqAAAAACgnAhUAAAAAFBOBCoAAAAAKCcCFQAAAACUE4EKAAAAAMqJQAUAAAAA5eRldgEAgKpt8uTJ2rx58zXHAwMD9cUXX1RiRVJoaKjGjh2rcePG3XDf2bNnKy8vT7Nnz9bQoUMlSWvWrKlwDT179lTnzp3117/+tcKvBQAwD4EKAOB0QUFBio+PL3Xstttuq+Rqymb37t2aMGGC2WUAAFwUgQoA4HTe3t4KDw83u4wyO3bsmFJTU9W1a1ezSwEAuCiuoQIAuIShQ4dq8uTJWrp0qe6++25FRkZq9OjROnnypN1+Bw4c0IgRI3TXXXepQ4cO+tOf/qTDhw/b7XP69GlNmjRJXbt2VUREhIYMGaJ9+/bZ7XPhwgVNnTpVnTt3VkREhMaPH68zZ87Y7bNr1y516NBB/v7+pdY8efJkPfnkk9q4caP69Omjtm3b6oEHHtDu3bvt9ktOTtbw4cMVERGhHj166P3337/qtYqLi7V8+XL16tVLbdu2VZ8+fexOLUxKSlKbNm00efJk27azZ8+qa9euGj58uAzDuE53AQDOQqACAFSKwsLCUr+uDAKffvqpNm3apL/85S+aOXOmDh48qKFDhyo3N1eStGfPHj3++OOSpLlz52r27NlKS0vTY489pqNHj0qSLl68qMcff1xff/21JkyYoPj4ePn4+Oipp57SL7/8Yvteb731li5duqRFixbphRde0M6dO/XSSy/Z1bxr1y517979useVlJSkN998U+PHj9drr70mT09PjRs3TufOnZMkpaena8iQIcrOzlZcXJyeffZZLViwQOnp6XavM2PGDC1evFj333+/li5dqr59+2ru3Ll67bXXJElt27bV008/rc2bN+urr76SJE2bNk3FxcX661//KovFUta3BADgAJzyBwBwupMnT6pNmzaljk2cOFEjRoyQJOXm5mrTpk1q0qSJJKl58+Z66KGHtGXLFj3++ON6+eWXdccdd2j58uXy9PSUJEVFRalXr15avHixFi1apM2bN+vkyZPavHmzwsLCJEkdOnTQgw8+qL1796pp06aSJKvVqvnz50uSunbtqsTERO3atctWV15envbu3auYmJjrHlt2drY2bdqk22+/XZJUrVo1DRkyRHv27FGfPn20atUqFRUVafny5apTp44kqVmzZnrkkUdsr/Hzzz9r/fr1ev755xUdHW07LovFomXLlmnw4MGqXbu2xowZo507d2rmzJmKjo7Wjh07tGjRIgUHB9/8mwEAcCgCFQDA6YKCgrRkyZJSxxo0aGD7c4cOHWxhSpJat26tJk2aaO/evXrggQd04MABjR071hamJKlmzZrq0aOHLQwlJCSocePGtjAlSX5+ftq2bZvd942MjLR73LhxY50/f972eM+ePQoMDFTLli2ve2x16tSxhSlJql+/viTZZtUSEhIUHh5uC1OS1L59ezVs2NDuexmGoZ49e6qwsNC2vWfPnlqyZIkSEhJ077336rbbblNsbKwGDRqkqVOn6qGHHlLfvn2vWx8AwLkIVAAAp/P29pbVar3hfqXNtNStW1fnzp1Tdna2DMNQYGDgVfsEBgYqOztbkpSVlaW6deve8HtVq1bN7rGHh4fd6Ye7du1St27dbvg6fn5+do9LTr0rLi6WJJ07d06NGze+6nlBQUG2P2dlZUmS+vfvX+r3uPL0wLCwMIWGhiopKUk9evS4YX0AAOciUAEAXEZmZuZV286cOaPbb79dNWrUkMViuWrhCEnKyMhQQECAJKlGjRo6ceLEVft89913qlWrllq0aHFTtezevVtTp04t2wGUonbt2qXWXBKipMuzbJK0evVqVa9e/ap9r5zNWrdunZKSktSqVSvNmTNHXbt2tT0fAFD5WJQCAOAyEhIS7EJVUlKSTpw4oa5du6patWpq27atPv74YxUVFdn2yc7O1ueff247ha9jx446fvy43cp/+fn5GjdunN57772bquPo0aM6ffq0unTpUuFj6tKli/bt22c3y3TkyBEdP37c9rhjx46SLgdKq9Vq+/rtt9+0aNEiW/g6efKkYmNj9fDDD2vp0qXKzs7WnDlzKlwjAKD8mKECADhdQUGB9u/ff83x0NBQSZevOxo5cqRGjRqlixcvauHChQoJCdGAAQMkSS+88IJGjBih6OhoDR48WJcuXdLy5ctVUFCgMWPGSJIGDhyoNWvWaNSoURo/frxq165tW9Fv8ODBN1Xv7t271alTp6tOCyyPYcOG6b333tOIESM0btw4FRUVaeHChXY3NA4NDdX999+vF198USdPnlTbtm31888/a+HChWrcuLGaNm0qwzA0depU+fn5aeLEiapVq5aee+45zZ07V3369FHPnj0rXCsAoOwIVAAAp8vIyNCjjz56zfEtW7ZIujxT06VLF9updj179tTEiRPl7e0t6fJqfCtXrtTixYv1/PPPy9vbWx07dlRsbKzuvPNOSZK/v7/Wrl2r+fPna9asWSouLlZ4eLjeeustuwUvrmf37t03XC79ZtWuXVvvvPOO5syZo8mTJ6t69eoaOXKkPvroI7v95s2bp2XLlundd9/VqVOnVLduXfXr10/PPfecPD099fbbb+urr77SK6+8olq1akm6fO+urVu3atq0aerQoYPttEcAQOWxGNwJEADgAoYOHSpJdjezBQDA1XENFQAAAACUE4EKAAAAAMqJU/4AAAAAoJyYoQIAAACAciJQAQAAAEA5EagAAAAAoJwIVAAAAABQTgQqAAAAACgnAhUAAAAAlBOBCgAAAADKiUAFAAAAAOX0/1N9vnp9CltrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_mean_token_accuracy' values from the 'results_df' DataFrame.\n",
    "# We omit any NaN values to ensure a smooth, continuous plot.\n",
    "plt.figure(figsize=(10, 6))  # Designating an optimal figure size for better clarity\n",
    "results_df['valid_mean_token_accuracy'].dropna().plot(color=\"darkgreen\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Mean Token Accuracy Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Mean Token Accuracy', fontsize=12)\n",
    "\n",
    "# Showcasing the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further fine-tune our Babbage model for 3 more epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = client.fine_tuning.jobs.create(\n",
    "    training_file=training_file.id,\n",
    "    validation_file=val_file.id,\n",
    "    model=job.fine_tuned_model,\n",
    "    hyperparameters={'n_epochs': 3}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ftjob-gpkNo58LXUqLfoqq6eB5TQYb'"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"object\": \"fine_tuning.job\",\n",
      "  \"id\": \"ftjob-gpkNo58LXUqLfoqq6eB5TQYb\",\n",
      "  \"model\": \"ft:babbage-002:personal::8EJokS4B\",\n",
      "  \"created_at\": 1698424950,\n",
      "  \"finished_at\": 1698426137,\n",
      "  \"fine_tuned_model\": \"ft:babbage-002:personal::8EKVvxZv\",\n",
      "  \"organization_id\": \"org-azQm6jrJ3FSBpJAwYcgxQxGu\",\n",
      "  \"result_files\": [\n",
      "    \"file-FParttlSDumS7dFkjW7UWXDF\"\n",
      "  ],\n",
      "  \"status\": \"succeeded\",\n",
      "  \"validation_file\": \"file-yF5vsOt3IMOhJs2jFeK46nWo\",\n",
      "  \"training_file\": \"file-OynKOPOBvIesnfq0Ubsu3p9n\",\n",
      "  \"hyperparameters\": {\n",
      "    \"n_epochs\": 3\n",
      "  },\n",
      "  \"trained_tokens\": 8500671,\n",
      "  \"error\": null\n",
      "}\n",
      "['file-FParttlSDumS7dFkjW7UWXDF']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_mean_token_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.73162</td>\n",
       "      <td>0.75781</td>\n",
       "      <td>0.74559</td>\n",
       "      <td>0.71875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.80441</td>\n",
       "      <td>0.71094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.89888</td>\n",
       "      <td>0.66406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.76620</td>\n",
       "      <td>0.72656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.82754</td>\n",
       "      <td>0.68750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  train_loss  train_accuracy  valid_loss  valid_mean_token_accuracy\n",
       "0     1     0.73162         0.75781     0.74559                    0.71875\n",
       "1     2     0.80441         0.71094         NaN                        NaN\n",
       "2     3     0.89888         0.66406         NaN                        NaN\n",
       "3     4     0.76620         0.72656         NaN                        NaN\n",
       "4     5     0.82754         0.68750         NaN                        NaN"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = client.fine_tuning.jobs.retrieve(job.id)\n",
    "print(job)\n",
    "results_df = None\n",
    "if len(job.result_files):\n",
    "    print(job.result_files)\n",
    "    results = client.files.retrieve_content(job.result_files[0])\n",
    "    with open('results.csv', 'w') as f:\n",
    "        f.write(results)\n",
    "    results_df = pd.read_csv('results.csv')\n",
    "\n",
    "\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AUlB866234tZbb0VtbS2+++47bNiwAevXr8dtt92Gzz77rM8+ZQBw9tlnY/v27fj8888HDKg2btwIh8OB+fPndwssLrjgAjzxxBNYs2YNLr/8ctk047zzzpMfk5iYCAC47777cNNNN/X7PtEM+5y/+MUvsGzZMpVHQxBErEA1VARBECHAcRwWLVqEjo4ObNq0SbbLPrH3FOtJ9eKLL+LMM8/EsGHD5FqQw4cPB/XeEyZMANCz1geQama6cuDAAezbtw+zZ8/G0qVLMW3aNDmYcrlcOHr0KIDu2aGu9U19MXLkSJhMJhw4cKDXXX9m2V1UVOTfhwoDY8eOBQC5RupE2BiLi4shCAJefPFF/P3vfwcg9RybPn067r77brz55psAgB9++AEAUFpaiieeeEKuOxoyZAgWLFiA5557DgsXLoTL5cL27dv7HduCBQuQmpqKN998E0eOHOnzcTabDX/7298AoEdtUGpqKs444wxs27YNu3fvxvbt23HmmWd2y8ix7+DE84KxcuVKPPPMMygrK+t3vFqHSS2ZxPFE1q9fj7///e99fg8EQRDBQAEVQRBEiCxYsAA6nQ7r16+XrapPPfXUbo9h0qkTex5t3bpVNmxwuVwBve+iRYvA8zyeeeYZNDQ0yLeXlZXJi38Gy2jU19d361Hl8Xjw+OOPy8FQ1/uYwUR/42IyuM7OTjz22GPdnl9VVSUHJsw8QQ3mz5+P1NRUrFmzpkcmcNWqVdi8eTNGjBiBqVOngud5fPnll3jhhRd6BKqs/oZlnCwWC1555RU8++yz3WrPRFHE8ePHAQxcg5Seno6lS5fCarXiV7/6FX766acej6mursb111+PI0eO4Be/+EWvlt+XXHIJPB4PHn74YQCSDLArU6dOxciRI7F+/Xp89tln3e7btWsXHn30Ubz88suqZhKVIDc3F7NmzcKePXuwYsWKbvdVVlbikUcewb///e8BpYMEQRCBQJI/giCIEMnJycGcOXPw2WefwWaz4Y477ujmRAZIC9zt27fjxhtvxHnnnYfk5GTs378f//vf/5CWlobGxsaAHfbGjBmD22+/Hc888wwWLFiA+fPnw+Vy4fPPP0dmZma31ysoKMDUqVPx008/4dJLL8XPfvYzuFwubNq0CUePHkVGRgaampq6PWfo0KEAgH/84x/48ccfe/SSYtx///3Yvn07Vq1ahT179uCUU05Be3s7vvzyS3R0dOD222/HjBkzAvps/rJp0yZcffXVvd6XkJCAF154AYmJiXjyySdx++234+abb8a8efNQWFgof/+pqan429/+Jh+z3/72t7juuutw3XXX4ayzzsKwYcNQW1uLdevWIT4+HnfddRcA4NRTT8Vpp52Gr7/+GhdeeCFOPfVU6HQ6fPfdd9i3bx/OOeccTJ48ecDPwKR5Dz30EK644gpMmzYNEydOhE6nQ1lZGbZs2QKn04nrr79ebu57Iqwn1Z49e5CTk4Of/exn3e7neR5/+ctf8Ktf/Qp33XUXZs2aheLiYtTX12PdunVwuVx4/PHHexiFRCOPPvoorrrqKixfvhzr1q3D5MmT0d7ejs8//xwWiwV33nmn3KiaIAhCCSigIgiCUIBLL70UX3/9NXie7+bux1i8eDF4nsf/+3//D6tXr4bRaEReXh5uv/12/PKXv8TcuXOxceNGeDwe6HQ6v9/3tttuQ2FhIV555RV8/PHHMJvNuPzyyzFz5kz85je/kR/HcRz++c9/4plnnsHGjRvx+uuvIzMzE6NGjcJDDz2E1tZW/O53v8NXX30lO/NdeeWV2L59O77//nscO3asm8lGV5KSkvD222/jP//5Dz799FO88847SEhIwJQpU3DNNddgzpw5AX6b/tPY2CibfPQ2Lsa8efOwcuVKvPDCC9i6dSs2b96M7OxsXHXVVbjpppu6mTxMnz4db775Jl588UWUlpZi/fr1SElJwfz583HLLbfgpJNOAiAFKU8//TRef/11rF69Gh9++CHcbjcKCwuxZMkSXHXVVX5/jvPOOw/Tp0/HO++8gy+//BIffvghnE4nhg4dioULF2Lx4sWybK839Ho9LrzwQqxYsQIXX3xxj4AekOz0P/zwQ7zwwgvYtGkTvv/+e6SlpWHWrFm44YYb5OMe7eTl5eGDDz7Aiy++iC+++AKvv/46kpOTMX78eFx77bXdLNkJgiCUgBOVtlMiCIIgCIIgCIKIEaiGiiAIgiAIgiAIIkgooCIIgiAIgiAIgggSCqgIgiAIgiAIgiCChAIqgiAIgiAIgiCIIKGAiiAIgiAIgiAIIkgooCIIgiAIgiAIgggS6kPlRRAEuN1u8DwPjuPUHg5BEARBEARBECohiiIEQYBer++1t19XKKDy4na7sWvXLrWHQRAEQRAEQRCERpgwYQKMRmO/j6GAyguLPCdMmACdTqfyaACPx4Ndu3ZpZjxE4NAxHBzQcRwc0HGMfugYDg7oOEY/sXIM2eccKDsFUEAlw2R+Op1OUyeH1sZDBA4dw8EBHcfBAR3H6IeO4eCAjmP0EyvH0J9SIDKlIAiCIAiCIAiCCBIKqAiCIAiCIAiCIIKEAiqCIAiCIAiCIIggoYCKIAiCIAiCIAgiSCigIgiCIAiCIAiCCBIKqAiCIAiCIAiCIIKEAiqCIAiCIAiCIIggoYCKIAiCIAiCIAgiSCigIgiCIAiCIAiCCBIKqAiCIAiCIAiCIIKEAiqCIAiCIAiCIIggoYCKIAiCIAiCIAgiSCigIgiCIAiCIAiCCBIKqAiCIAiCIAiCIIKEAiqCIAiCIAiCIIggoYCKIAiCIAiCIAgiSCigIgiCIAiCIKIaj0fE7iMOOF2i2kMhYhAKqAiCIAiCIIioZs0WC+78ax3eWteu9lCIGIQCKoIgCIIgCCKqqax1AQB2H3GoPBIiFqGAiiAIgiAIgohq2joFAEB5tVPlkRCxCAVUBEEQBEEQRFTTZpECquZ2AW0Wj8qjIWINCqgIgiAIgiCIqKbdm6ECgKPVLhVHQsQiqgdUDocDDz30EKZPn47Zs2djxYoVfT528+bNuOiiizBlyhRcd911KCsr63b/9OnTUVxc3O2/zs7OcH8EgiAIgiAIQkW6ZqXKayigIiKLXu0BPPnkk9i9ezdee+01VFdX44EHHkBubi7OPffcbo87dOgQbr75Ztx000248MIL8d577+Haa6/FZ599hsTERNTV1aGjowMbNmxAfHy8/LyEhIRIfySCIAiCIAgignTNUJVThoqIMKpmqKxWK1auXInf//73GDduHM466yzccMMNeOONN3o89q233sKUKVNw1113YeTIkbj//vuRlJSE1atXAwCOHDmCrKws5OfnIysrS/6P47hIfyyCIAiCIAgiQjhdImwOX/8pkvwRkUbVgGr//v1wu92YMmWKfNu0adOwY8cOCILQ7bGVlZWYOHGi/DfHcSgqKkJpaSkA4PDhwygsLIzIuAmCIAiCIAht0N7Z3YSivNoJUaQGv0TkUFXy19DQgLS0NBiNRvm2zMxMOBwOtLa2Ij09vdvtdXV13Z5fW1uLlJQUAFKGymaz4eqrr0Z5eTnGjBmDhx56KOAgy+PRhjMMG4dWxkMEDh3DwQEdx8EBHcfoh47h4CAcx7Gl3Q0AMJs4WB0iLDYR9c0uZKbqFHsPwkes/BYD+XyqBlQ2m61bMAVA/tvp7N5H4LzzzsOtt96KCy64AHPmzMHq1auxa9cunHzyyQCAsrIytLW14d5774XZbMZLL72E6667Dp988gnMZrPfY9q1a1eIn0pZtDYeInDoGA4O6DgODug4Rj90DAcHSh7Hw9UGAKlIiHMhIQ6ob9Vjw+b9KBlG0r9wQr9FH6oGVHFxcT0CJ/Z3V2MJAJg7dy5uu+023HHHHfB4PDj55JNx8cUXw2KxAAD+85//wOVyITExEQDw1FNPYd68efjqq69w4YUX+j2mCRMmQKdTf0fD4/Fg165dmhkPETh0DAcHdBwHB3Qcox86hoODcBzHNtEGoBk5GQlIS9ahfrsNOtNwTJ6cpMjrE92Jld8i+5z+oGpAlZOTg5aWFrjdbuj10lAaGhoQHx+P5OTkHo+/5ZZb8Otf/xodHR3IyMjAXXfdhby8PABSZqtrtisuLg7Dhg3rIRMcCJ1Op6mTQ2vjIQKHjuHgQOnj2GkTsPKLdpw+LREjhhoUe12if+j3GP3QMRwcKHkcO6xSvVSyWYeReUZs3G5DRa2HzpMwQ79FH6qaUowZMwZ6vV42lgCAbdu2YcKECeD57kNbs2YN/vznP8NoNCIjIwN2ux1bt27FySefDFEUceaZZ+KDDz6QH2+1WlFRUYGRI0dG6uMQBEH4zdc/WfHfte149ZM2tYdCEAQR1TDL9BQzjwLvBhVZpxORRNWAymQyYcGCBVi6dCl27tyJDRs2YMWKFbjmmmsASNkqu90OACgoKMDbb7+NdevW4ejRo7jvvvswdOhQzJ07FxzH4bTTTsOzzz6LrVu34tChQ/jd736HIUOGYN68eWp+RIIgiF5p7ZCKXWsa3SqPhCAIIrpps3gDqkQdCnOlgKqixgWPQE5/RGRQNaACgCVLlmDcuHG49tprsWzZMtxxxx04++yzAQCzZ8/G2rVrAQDjx4/H0qVL8cQTT2DhwoUAgBdeeEHOZN1///0455xzcN999+Gyyy6D2+3Giy++SKlIgiA0SaddutDXN1NARRAEEQrMNj05kUdulh5GAweHS0QtbVgREULVGipAylItX74cy5cv73HfgQMHuv29aNEiLFq0qNfXiYuLw4MPPogHH3wwLOMkCIJQEqtN2lFttQhwOAXEGVXf3yIIgohK2i0+yZ+O5zBiiB6HKl0or3YhL5tqVInwQ1dwgiAIFbDafc3LG1oHdy8PgiCIcMIkf8mJ0rK2MFcyKSuvoToqIjJQQEUQBKECFpsvoKpvoYCKIAgiWJjkL8UslXkUeOuojpIxBREhKKAiCIJQAavdVyzd0EI6f4IgiGBp6zwxQyUFVGUUUBERggIqgiAIFegq+atvpgwVQRBEMLjcorxBlWL2BlRe6/SqOhecLnL6I8IPBVQEQRAq0NlN8kcZKoIgiGBgPah4DjCbpGVtVpoOifEcPAJQVU9ZKiL8UEBFEAShAp1dJH9UQ0UQBBEcrH4qKZEHz3MAAI7j5DoqavBLRAIKqAiCICKMKIrdJX8UUBEEQQSFr6lv9yUtc/ojYwoiElBARRAEEWFcbsDdJYaqb3ZDFEnnTxAEESiyZbrX4Y/BjCnIOp2IBBRQEQRBRJiulukAYHOI6LRRQEUQBBEosmV6jwwVSf6IyEEBFUEQRIRhcr/EeE62+SVjCoIgiMA5sakvo8Dr9FfT6IbNIfR4HkEoCQVUBEEQEYZZ/CbE88hOk2QqVEdFEAQROMzlj1mmM1KTdEhLlm6rINkfEWYooCIIgogwTPKXYOKRlaYHINVREQRBEIHRZpE2o5ITdT3uY/2oSPZHhBsKqAiCICKM1eaT/GWnS4uABspQEQRBBExfGSoAKPA6/VFARYQbCqgIgiAijFxDZeKR7c1Q1VENFUEQRMC0dTKXv55LWjKmICIFBVQEQRARprOXGirKUBEEQQSOnKHqTfLnDaiOUg0VEWYooCIIgogwnV0lf2RKQRAEETRyDVVvkj9vDVVTm0d+HEGEAwqoCIIgIkxXyR8zpWhocUMQqBcVQRCEv7g9vh5+J/ahAiQVwJAMadOKslREOKGAiiAIIsJ0lfxlpurAc4DbA7R2UK8UgiAIf2FyP44DzAm9L2lZluoo1VERYYQCKoIgiAjDJH8J8Rz0Og7pKdIOKhlTEARB+A+T8SUl8NDxXK+PKSSnPyICUEBFEAQRYZjkz2ySpmAypiAIgggclqFK7kXux5Cd/kjyR4QRCqgIgiAiTFfJHwDZOr2eMlQEQRB+02bpuwcVQ3b6q3ZBFKlOlQgPFFARBEFEGJahSoiXJCpZzOmvmTJUBEEQ/uLLUPW0TGfk5xjA80CHVUBjG82xRHiggIogCCLCyLbpXslfTjplqAiCIAKl3VtD1V+GymjgMCxLmmPJmIIIFxRQEQRBRBirV/KX6JX8ZVENFUEQRMC0+VFDBQAFrI6KAioiTFBARUQFZced1JSPGBSIouhz+TNJkj9q7ksQBBE4TPKXYu5b8geQ0x8RfiigIjTP8XoXbnysFo+82Kj2UAgiZBwuER5vuymWocr2Sv6a2z1wualomiAIwh/YRutAGaquxhQEEQ4ooCI0z9EaF0SRupwTgwOrTQqYOA4wxUkZqlQzD4MeEEWgsZWyVARBEP4gZ6j8DahqXBAE2rQilIcCKkLzNLdLC8z2TgFuD02ERHTT2cXhj+OkgIrjOLJOJwiCCBB/bNMBIDdLD4NeUgjUNNEcSygPBVSE5mnpEOR/t3bQ7j0R3TDLdCb3Y5AxBUEQRGD4Y5sOADqew4ihJPsjwgcFVITmae7SN6K1S3BFENFIp617U1+GnKFqpt1TgiCIgfB4RHRY/ctQAUDhUHL6I8IHBVSE5mGSPwBooQwVEeUwyV+i1+GPkZ1OTn8EQRD+0m71bbAmJfgRUJHTHxFGKKAiNE/XIKqlnRabRHTTl+SPaqgIgiD8p91bP5WUwEOn4wZ4NPWiIsILBVSE5mlu9+1CtZDkj4hy+pb8UYaKIAjCX9o6/bNMZzCnv8o6F7WnIBSHAipC87SQ5I8YRPQl+SNTCoIgCP9p99Phj5GdpkNiPAePAFTVU5aKUBYKqAhNY7MLsDt9O0kk+SOiHauN2ab3LvnrsAqw2SkTSxAE0R9tssOff0tZjuNI9keEDQqoCE3TfEIA1WqhhSYR3Vjt0gZBoqn79Jto4uWsFcn+CIIg+kdu6mvu3zK9K2RMQYQLCqgITXNiQEUZKiLasXRp7HsiZExBEAThH22WwGqoAKCAelERYYICKkLTMBOKeCPX7W+CiFaY5O/EDBXgq6OiDBVBEET/tLEaqgACKmZMUV5DARWhLBRQEZqGZajYJNja4YEgkDsPEb3Ikr/4ntNvjjdD1UAZKoIgiH5hkr/kgCR/0lqiptENm4M2aAnloICK0DRM4leYJ02CHgFyZ3SCiEY6bX1L/liGqq6ZMlQEQRD9EYzkLzVJh7QkHqIIVFCWilAQCqgITcMyVNlperkTOsn+iGjGZ5vec/rNTqcMFUEQhD/4TCkCW8oWkOyPCAMUUBGahjX1TU+WdpUASfZHENFKf5I/au5LEAThH+0B2qYzCsmYgggDFFARmoY18k1L4pGaJC02yemPiFZEUZQzVP1J/upbPBBFqhUkCILoDY8gyvL/QGzTAaCArNOJMEABFaFpmOQvPVmHtGRvQEWSPyJKsTtFCN7Tt7cMVVaqJPlzukR595UgCILojsUqgO05BZyhoua+RBiggIrQLKIoytmotC6SP8pQEdEKk/vxHBAf1zNDZTRwSEuWznMypiAIgugdZpmeaOKg1/WcS/uD9aJqavOgvZPmWUIZKKAiNEunTYTLW5sv1VB5M1QWmgCJ6KSr3I/jel8EZJN1OkEQRL/4DCkCk/sBkiFQTrr0vKNkTEEoBAVUhGZhcr9EE+fduWc1VCSFIqIT2TK9F4c/BhlTEARB9E8wluldIdkfoTQUUBGapWv9FACf5I9c/ogohUn+zL3UTzGyvBmq+mbKUBEEQfSGnKEKMqBixhTk9EcoBQVUhGaR66e8Uj/m8ke26US04k+GiklR6lvpPCcIgugNVkMVdIbKW0dVRgEVoRCqB1QOhwMPPfQQpk+fjtmzZ2PFihV9Pnbz5s246KKLMGXKFFx33XUoKyvrdv+aNWtw5plnYtKkSbjtttvQ3Nwc7uETYaRHhqqL5I8spYloxNqPZTojS66hooCKIAiiN5iZRDA1VIBP8ne02kXrCUIRVA+onnzySezevRuvvfYaHnnkETz33HP47LPPejzu0KFDuPnmmzF//ny8//77GDt2LK699lp0dnYCAHbu3Inf//73uP322/HOO++gvb0dS5YsifTHIRTE19RXOk2Z5M/hEmFz0ARIRB8sQ9WbZTojh9VQkeSPIAiiV1iGKsUc3DJ2+BADeA7osApoaqPNKyJ0VA2orFYrVq5cid///vcYN24czjrrLNxwww144403ejz2rbfewpQpU3DXXXdh5MiRuP/++5GUlITVq1cDAF5//XWcd955WLBgAUpKSvDkk0/im2++QWVlZaQ/FqEQclNfb2bKFMcj3sh1u48goglWQ5XYj+SPNfdtbPPA46GNA4IgiBNhNVTBSv6MBg552ZIagIwpCCVQNaDav38/3G43pkyZIt82bdo07NixA4LQ3cmtsrISEydOlP/mOA5FRUUoLS0FAOzYsQPTp0+X7x86dChyc3OxY8eO8H4IImycKPkDuhhTkNMfEYV0+iH5S0vWQccDggDaOSUIguiFthBs0xmy7I+s0wkF0Kv55g0NDUhLS4PRaJRvy8zMhMPhQGtrK9LT07vdXldX1+35tbW1SElJAQDU19cjOzu72/0ZGRmora0NaEwejzYWMGwcWhmPGjS3MY00J38PqUk8apo8aGpzweNR9fQdEDqGgwMlj6PFKr2GKa7/18tK1aG22YPaJicyUgJrWkn0Dv0eox86hoMDJY5jm1elYjYF/zoFQ/TYCKDsuJPOqQCJld9iIJ9P1RWpzWbrFkwBkP92Op3dbj/vvPNw66234oILLsCcOXOwevVq7Nq1CyeffDIAwG639/paJ77OQOzatSvQjxFWtDaeSFLfnA5Ah4aawyj1dvjlhGQAcdi1rwJm0a7q+Pwllo/hYEKJ43i8JglAPFqbalBaWtbn40yGFABG/FBaBle7I+T3JXzQ7zH6oWM4OAjlODa3ZwDgUVN5EGJncIt60WEEkIK9h9tQWloR9FhiGfot+lA1oIqLi+sR8LC/4+Pju90+d+5c3Hbbbbjjjjvg8Xhw8skn4+KLL4bFYun3tUwmU0BjmjBhAnS64FPISuHxeLBr1y7NjCfSCIIIyyvVAIBTpo9BZqr0HRTsa8HeY1aYU3IxeXKymkMckFg/hoMFJY/ju982AnDgpFHDMHlyYp+PK9zRjPI6GxKSh2Hy5KSQ3pOQoN9j9EPHcHAQ6nEUBBG2FdL6YMa0schICe5cyBjqwv/7sh4N7UZMnDgJPE9qAH+Jld8i+5z+oGpAlZOTg5aWFrjdbuj1XqvghgbEx8cjObnnYvmWW27Br3/9a3R0dCAjIwN33XUX8vLy5NdqbGzs9vjGxkZkZWUFNCadTqepk0Nr44kUFpsHrIwuI9UAnU6a6NJTpPOkzSJGzfcSq8dwsKHEcbR5k03mBH2/r5WTbgBgQ2ObQOeOwtDvMfqJpmPY3ObB8x+04MI5ZkwcHT/wE2KIYI9jp90DwevXk5bsWx8ESn4OD4MesDtFNLQBuZnRcU5piWj6LYYbVU0pxowZA71eLxtLAMC2bdswYcIE8Hz3oa1ZswZ//vOfYTQakZGRAbvdjq1bt8qSv0mTJmHbtm3y42tqalBTU4NJkyZF5LMQysIMKZITeei7TJap3gJUcvkjohHZNr0flz8AyJat0+k8J4ho5osfO/HFD1a8/mm72kMZNDDL9MR4DgZ98FklnY7DiCGSMUX58cDKQwjiRFQNqEwmExYsWIClS5di586d2LBhA1asWIFrrrkGgJStstulOpmCggK8/fbbWLduHY4ePYr77rsPQ4cOxdy5cwEAV1xxBT766COsXLkS+/fvx+9+9zucdtppyM/PV+3zEcHj60HVfeeD9aRq6SCXPyL6YC5/if24/AFAdrqUia1voV5UBBHN1DRKv2Gy5laOUC3Tu1Lgdfqj40OEiuqNfZcsWYJx48bh2muvxbJly3DHHXfg7LPPBgDMnj0ba9euBQCMHz8eS5cuxRNPPIGFCxcCAF544QU5kzVlyhT88Y9/xD//+U9cccUVSElJweOPP67OhyJCpqWd9aDqfoqmJem63U8Q0YTVm6FK8DND1dBC5zlBRDO1TVJA1dTmQZuFfs9KwL7H5BAs0xmFuZKZWTlZpxMhorrvtMlkwvLly7F8+fIe9x04cKDb34sWLcKiRYv6fK2FCxfKwRYR3fTWgwoAUr0BVStJ/ogoQxRFX2Pf+P4DKtbct9UiwOEUEGdUfe+LIIggqGnyXavKq12YXET1JqEi96BSIENVONTbi4oyVESI0FWa0CR9BVQsY2WxiXC6xIiPiyCCxe4Q5ULqRFP/kr+kBB7xcdJjGlpp84AgohFRFFHX5JPtlh2nRbsStHtrqJLNykn+jtW64HLTmoIIHgqoCE0iS/6SugdUSQk8dN6zlrJURDTB6qd4Hogz9B9QcRyHbG+rgHqS/RFEVNJqEWB3+hbp5dVkfKAEbXINVejZvpx0HRLiOXgEoKqeAl4ieCigIjSJz5Si+ynKcZxP9mchYwoieugq9+O4gZ2pZGOKZjKmIIhopLap+2+XjA+Uod3byDdFgQwVx3EoINkfoQAUUBGaxGdK0XMHisn+msmYgogimGV6wgAOfwwypiCI6KbW6/DHFv7l1S4IAsnKQoXZpivh8gcAhczpj4wpiBCggIrQJKzP1Ik1VACQnkS9qIjoQ7ZMH8Dhj5GVJmWo6sg6nSCikhpvhmpqSTz0OsDmEFFHveVChtmmpyjg8gd0cfqjDBURAhRQEZrD4xFlOV9vGSqf0x9J/ojowV+HP0Z2OmWoCCKaqfM6/A3L0mO4t4FsGdVRhUy71zZdCZc/wGdMQZI/IhQooCI0R6tFgCgCPNe7RjotydvclyR/RBTBMlT+S/5Yc186zwkiGmEZqiGZeoxksjJy+guZcEn+qhvdsDloo5YIDgqoCM3BAqWUJB46vufik2WtSPJHRBOshspfyR+roapvdkMUqe6CIKINZkoxNEOPwjxqIKsEgiCi3cokf8osYdOSdEg18xBFyT6dIIKBAipCc/TVg4pBkj8iGglU8sea+9ocIjptFFARRDQhCCLqvA6dQzL0PuMDylCFRKddhOC99Cthm85gsj+qoyKChQIqQnMMFFCR5I+IRgJ1+Ys38rKkpY6s0wkiqmhq98DllvrOZaXqZMlfZR01kA0FVj9liuNgHKCfXyAUUkBFhAgFVITmaPFmnk5s6stIJ8kfEYXINVR+Sv4AMqYgiGiFGVLkpOmg03HIStMh0SQ1kCVZWfD4mvoqu3xlTn9HSZJJBAkFVITm8GWoej89meSvzSLAQz09iCjBagtM8gd0NaagDBVBRBM13h5UORnSb5jjOIwke+6Qabcoa5nOoAwVESoUUBGao7+mvgCQaubBcYAg+vpREITWscp9qPyXqbA6KnL6I4jooqshBYMt2sto0R40bZ1e0yqFDCkYBUOlY9PY6kGHldYVROBQQEVojoFqqHQ6Tk73Ux0VES34bNP9n3ZzKENFEFEJC6iG9BJQlR+nXlTBorRlOiPRxMsS66PUK4wIAgqoCM0xUIYKIKc/IvpgTn3+2qYDvgwV1VARRHRR01tAlUeyslBhqhSlmvp2pXAoHR8ieCigIjSHz5Si79NTdvojYwoiSpAlf366/AFATro3Q0UufwQRVciSv8yuGSqphqq+xQMLycqCos3r8pescA0VQHVURGhQQEVoCqdLlHeg+pL8Ab7sVTNJ/ogoIRjJn5yhavVAIAMWgogKPB5RrnvMyfBdx5ISeGSlSn+Xk6wsKMKZoSog0xAiBCigIjRFqzfjpNdJF5++YJbqLST5I6IAQRADbuwLAJkpOvAc4PbQuU4Q0YK0AQIY9EDGCRuDTPZHxhTBEa4aKsCXoTpa44Io0gYWERgUUBGagkn4UpN04Pm+pVFM8tdKkj8iCrA7RbDrcyAufzodh4wU5vRHsj+CiAaY3C8nXd/jOjZSNqaggCoY5AxVGCR/w3P04DnpPZrbaQOLCAwKqAhNwSax/uR+QJcMFUn+iCiAyf10PGA0+B9QAWRMQRDRRm+GFIxCkpWFRLhs0wEgzsgjL1s6ZiTJJAKFAipCUwzU1JfBaqhIBkVEA9YuDn8cF1hAxYwp6siYgiCigtrGnj2oGCNlyZ+TZGUBIoqi3Ng3HJI/gIwpiOChgIrQFLJlelL/GapUkvwRUURnEA5/DMpQEUR0IUv+Mnpex/JzDOB5qY0C/aYDo9MuwuPdQw1XQMUa/B6lgIoIEAqoCE0xUFNfhs+UwkO7fITm6bR5Hf4C6EHFyKbmvgQRVdQ2Sdex3jJURgOH/BwypggGZpkeb+QQZwxXhsoryayhY0MEBgVUhKZo9qOpL+AzpXC5fQ1TCUKrMIe/QCzTGdlpzJSCdrMJIhqQa6gyewZUgM+YgrIggcEMKZLDUD/F6Or0R60qiECggIrQFKwmaqAaqjgjjwSvfIqa+xJah2WogpH8ZXtrqEgeRBDax+kS0dQm/VZ7M6UAfIv2MjI+CIhwWqYz8rL0MOgBu0NEbTPNuYT/UEBFaIoWPzNUQHfZH0FoGbmGKijJn6+JtctNO6YEoWXqW9wQRUmWltpHJoX1oiLjg8DwNfVV3jKdodNxGD6EHR8KeAn/oYCK0BT+1lABXZz+qF8EoXFCkfylmHkYDRxEEWhspc0DgtAyPkMKfZ+OniO9dTrHal1we2iTxF9YDVU4JX8AUEjGFEQQUEBFaAa7U5AXnv5kqNjuHzn9EVrHGoLLH8dxyEql5r4EEQ34DCn6voblpOtgiuPgcgNV9fSb9hdmmZ4SRskfABSQdToRBBRQEZqBZZqMBs6vhaevFxUFVIS2kWuogpD8AUB2ujegIk0/QWiamsa+m/oyeJ7z9Ts6TrIyf5Elf+bwSf4AXwaRMlREIFBARWgGFhilJfnX/JQ5/ZHkj9A6nSFI/gCfdXoDZagIQtPUNg8cUAFdjSlo0e4vbZ1eyV+EMlTH6kiSSfgPBVSEZmhu879+CiBTCiJ6CEXyB/ia+9aR0x9BaJpaPzJUQJd+RxRQ+Y0s+QtzDRWTZLo9JMkk/IcCKkIzBGJIAZDkj4ge5Ma+lKEiiEENM6UY2kcPKgZJ/gKnrTP8tumAVLcq96Mipz/CTyigIjQD60HljyEF0EXy10GSP0LbMMlf0DVU1NyXIDSP3SnI16OBMlQjvdbpNU0eOYNN9E+kaqgAMqYgAocCKkIz+DJU/p2WqV7JH7n8EVrHGrIphbQ4q2+mDBVBaBXm8Jdo4pCU0P9vPcWsk691R2to0T4Qoij6bNPDnKECfNbpVONG+AsFVIRmaA6gqW/Xx1ntIhxO2uEjtAvbgU4IsoaKZagsNpF2swlCo/hbP8VgdVRlx2nRPhA2hwi3d+803DVUgO/YkNMf4S8UUBGaoYUFVEn+BVSJ8RwM3usWyf4IrSIIIqyO0CR/CfE8Ek1SMEayP4LQJv46/DGY7K+c6nQGpM1rSBFn4BBvDP/SlUn+qhvdsNOGLeEHFFARmqElQFMKjuPI6Y/QPDaHCNHrvJsYpCkFAOSQMQWhIMfrXXC6yBJaSVgPqqF+Z6iYMQVlQQYiknI/QKrRTjHzEEXgWC3NucTAUEBFaAJRFNHs7SeVnuJ/wakcULVTQEVoEybR0+sgZ1SDIYuMKQgFsDsF/O3NZly9tAZPvNak9nAGFXUBZ6i8kr9qF0SRgtv+8BlSRGbZynGcXEdFGUTCHyigIjSBzSHC4d0tZe59/pCWTE5/hLaxdLFM96dhdV+QMQURKuXVTty6vA5rNlsAAD/us9FCXkFq5Boq/zYFRwzRg+ekYKGZGtT3C5P8RSpDBQCFeeT0R/gPBVSEJmCGFKY4DqY4/09LcvojtI41RMt0BlmnE8EiiiJWb+rALcvrcLTGhfRkHga9ZHJyvIECdKVgLn8D9aBixBl55GZJj6UsSP+0d0rfbSQs0xlkTEEEAgVUhCYItKkvgyR/hNZhkr/EIB3+GFlUQ0UEgcUqYNnLjfj7Wy1wukTMHBePl34/FCflS4vFAxW0kFcCi01Ah9Xbgyrdf23vSMqC+EWkmvp2pWAoHRvCfyigIjRBS3tgTX0ZJPkjtA5r6psQgiEFAORQhooIkL3lDtz4WA02brdBxwO/WZiKx27JQlqSDiUjpIBq31EKqJSgrkna6Egx8zAF8Fsn63T/aLdEtoYK8Dn9NbR6YLHSGoPonxBKpAlCOQJt6ssglz9C63SG2NSXkcVqqFo8EEUxpHosYnAjCCLe2dCB/3zcCkGQJGj/d30GSgri5MdI/7Zg/1GHegMdRMj1UwFkp4AuTn+UBekXX4YqcpI/s4lHdpoO9S0eHK1xYfyouIGfRMQsFFARmiDQpr4MX0BFu0eENlFK8peZogPHAU6XiDaLINcPEkRXmts8ePy1JmzbbwcAnD49Afdekd4joC8pkDIjhyqdcHtE6HUUoIeC3IPKz/opBpP8Ha1xwSOI0PF0HHqD2aZHMkMFSFmq+hYPyo47KaAi+oUkf4QmYBmm9AAXibLkj2qoCI3S2cXlLxSMBk52wCTZH9EbP+y14cbHarBtvx1xBg73/zIdf/hVRq/Z0bwsPcwmDi43yc2UoFbuQRXYNWxoph5xBg5Ol4hqMgjpk3YVaqgA+KzTa8LzG3G5RWzZacW+csoURzuUoSI0QXNbcBkqtkvf3inA4xGho11WQmPINVQhSv4AyZiiud2JhhY3ioYbQ349YnDg9ohY8XEr3l7fAQAYmWvA//06EyO8i8He4DgOxSPisG2/HfuPOuh8CpHaZukaluNnDyqGjudQMNSAA8ecKK92IT+n72MWy7TJNVSRzcwzSabSTn+VdS6s3WLB5991otUiwGjg8M6fcyP++QjloICK0ARMspcWYA1VciIPngMEEWi1CMgIoCkwQUQCqzdDZQ5R8gcAOek6HKgA6popQ0VI1DS68acVjbK5xMVzzfjNwlTEGQeeS0sKjFJAVeHEReEe6CDHl6EKfFlVkCsFVGXHnZg7JUHpoUU9oijKtumRzlAVeE1Dyr3Nl0OpXXW6RGwqteKTzRaUHnL0uO9/O20471RzSOMl1IMCKkITtARpm67jOaQk8WhpF9DS7qGAitAcnd4aKqUyVABZpxMSX/3Yib+92YxOuwizicP9V2dgzmT/F+Ssjoqs00NDFEXUNAVXQwWQdfpA2B0iXN4pLyXCAVXX5sst7QLSg1hjVNS48MkWC9Zt7ZSlizwHzBwXjwtmm3HwmBP/XduOTaVWCqiiGAqoCNURRdFXQxVgQAUAaWadFFCR0x+hQawK2aYDXZr7ttK5HsvYnQKeW9mCtVs6AQDjRhrx+19lYkiA2ZGSEVKR/dEaF6x2QZFzNBZp7xRgc0i/80Bd/gBy+hsI5vBn0APxcZGV9bPmy1X1bpTXuPwOqBxOAd9st+GTLRbsOuzLRmWn6XD+LDPO+1mivEGWm2XAf9e2Y9t+OzptQsiOsIQ6qB5QORwOLFu2DOvWrUN8fDyuv/56XH/99b0+dv369fjb3/6G2tpalJSU4A9/+APGjRsHAGhra8PMmTO7PT41NRVbt24N+2cgQsNi8+0+pQXhXJaWrAOqXeT0R2gSn2166AuBbO8FuL6ZMlSxStlxJx5d0YSKGhc4DrjynGRc9/OUoOpHM1J0si30oWNOTCqKD8OIBz+13uxURooORkPgx2GkV1Z2vMENu1NAvB9yzViCZXVSzDpV2kUU5hqkgKraiWkl/f9Gyqud+GSzlI2y2KQgm+eBn4034eezzZgxNr6Hk+OIIXrk5+hRWefGd7ttmD8jMWyfhQgfqgdUTz75JHbv3o3XXnsN1dXVeOCBB5Cbm4tzzz232+MOHTqE++67D3/84x8xdepUvPrqq7j55puxfv16mEwmHD58GKmpqVizZo38HJ6nSSkaYIYUZhMX1MWIOZ+R0x+hRXy26cplqBrI5S8gdh62Y/l/m3DuFCMmT1Z7NMEhiiLWbLbgn++1wukSkZ7M46FfZWJqcWhBUEmBEfUtNuyroIAqWGqavIYU6cFJztOSeaSYebRZBFTUuFA8guy5u8Is0yNdP8UozDVgU6mtT2MKu1PA19usWLPZgr3lPvlsTroOP59lxrk/S0Rmat/LbY7jMHdyAt74XJL9UUAVnagaUFmtVqxcuRIvvfQSxo0bh3HjxuHQoUN44403egRUW7ZswejRo7FgwQIAwL333os33ngDhw8fxoQJE1BWVobCwkJkZWWp8EmIUGgOsn6KkUrNfQkN06mk5M8rJ2ps9ZCrZQB885MVNY0e/HQ4DleqPZggsFgFPPVGEzZutwGQai8euCYjqIz+iZSMiMPG7TZq8BsCLEM1NIj6KUBaUBfmGlB60IGyagqoTsSXoVInoOpqTNGVI1VOrNlswYbvO+V5XscDp0404YLZZkwriQfvZ1+xOVOkgOr7PXbKUkYpqgZU+/fvh9vtxpQpU+Tbpk2bhn//+98QBKFbhik1NRWHDx/Gtm3bMGXKFHzwwQcwm80YPnw4AODw4cMoKCiI9EcgFIAFQoFapjPY81pJ8kdoEFnyp4DLX1oSD70OcHuApjaPHGAR/cP6+9S3Rd/3tafMgT+taERdswd6HXDjglQsOj3J74XaQBSPkBaL+8mYImiYw1+gNWxdGekNqMqpJ1gPmGV6cqI6plNde1F12gR8/ZOUjepq5jI0U4+fz0rEuaeYgzKuOCnfgCEZOtQ2efDDXntA5jKENlD16tLQ0IC0tDQYjb7+F5mZmXA4HGhtbUV6erp8+/nnn48vv/wSV155JXQ6HXiexwsvvICUlBQAwJEjR+B2u3HppZeirq4O06dPx5IlS5CdnR3QmDwebWQ52Di0Mp5w0tQqXYzSkvigPm+KNzve3O7W1PcVS8dwMBPKcfQIolysHm8UFTkXMlN0qG32oLbJiYwUylD5Q7V3wdvYpoPLFT31Z6u+seD5D9ogCMDQTB1+f106ikcYIYoClJpWRuXpwHFAfbMHjS3OoDe2IoFW59SaJikIyk4L7hoGACOGSsuxsuNOzX0+pQn0OLZ2SL/Z5AROle9mSAYHg15yG7xsyXHYndKcrtcBsyaacP6pCZhcFCdvcgQ7xlkTTXj/Kws2/tSJUydoO0up1d+i0gTy+VQNqGw2W7dgCoD8t9PZfbespaUFDQ0NePjhhzFp0iS89dZbWLJkCT788ENkZGSgrKwM6enpWLJkCURRxN///nf85je/wcqVK6HT+X+B2LVrV+gfTEG0Np5wsP9wIoAEuB1NKC09FvDzmxuMAFJQXWdBaWmV4uMLlVg4hrFAMMfR5uQAZAIADh/cBb0Ca9UEYwoAI37YXgZXO8m0BkIQgZrGTAAcXB4Om7/bi7Qk7WezrQ4Oz7+fAUHkMHmkHYtmWWBrqUVpi/LvlZ2ShrpWPT79+gDGDtd+pkprc2rF8TQAenS2HkVpaXAZJrdFDyANB4/ZUFpaquTwNIu/x/FIhRmACVZLPUpLj4Z1TH2RnZKK400G2J0iMpPdOLnYjukn2WE2iYAd2Lkz9PcYkiidA1t2dOLHbccUuV6EG639FtVE1YAqLi6uR+DE/o6P714c+9RTT6GoqAhXXXUVAODRRx/Feeedh/fffx833XQTPvnkE3AcJz/vmWeewezZs7Fjxw5MnTrV7zFNmDAhoAAsXHg8HuzatUsz4wkn63e3ALCiaOQQTJ6cFPDzE9KdWLGuAQ5PHCZrqOI8lo7hYCaU41jf4gZQB4MemD5tsiLjKdzRjLJaGxJShgX1e4k16lvccHvq5L8T00dj8jjty2n2lDkgiI3IStXhybtHhtXdbNLuFqzbaoVTNwyTJyeH7X1CRYtzqiiKaP1vNQBg9szioOuoih0Cnl1dA4uNR8GoCXJt8GAk0OP4yfZmADYUjcrF5Mnq9Gn6bbIDm0vt+NmEeEwcbQzL73GiIOKtjbVobgeE+BJMHqddkxgt/hbDAfuc/qBqQJWTk4OWlha43W7o9d6GlQ0NiI+PR3Jy90l9z549uPrqq+W/eZ5HSUkJqqulicxkMnV7fEZGBlJTU1FXV4dA0Ol0mjo5tDaecMBqnzJS9UF91sxUg/w6HMcrVlugFLFwDGOBYI6j3SnJBRLjecXOgZwMAwAbGloFOq/8oK65e8bgeEN0fG9V9dK8OHyIQb4+hosxhXFYt9WKg8dcUfHdaGlObW7zwOmSGrUOyTQGbRRjTtBhaKYeNY1uHKsTkJFqHPhJUY6/x5GZUqSag1sjKMGkkxIw6aTwbsTodMCcyQn4aKMFW3ba8bOJ2nf709JvUW1UtREZM2YM9Hp9t/T2tm3bMGHChB6W59nZ2Thy5Ei328rLyzFs2DBYLBbMmDED3333nXxfXV0dWlpaMHLkyLB+BiJ0QnX5SzFLz/MIQIdV+1IeInbo9PYhSVCwUWMWWacHRE1j95qpyvroqKE6VicFgsOHhH/fc0yBVK+x/6gToiiG/f0GEzVeh7/MNB30IbpujqQGv73SprLLXySZM0UK2jbvsMHjod9iNKHq2WkymbBgwQIsXboUO3fuxIYNG7BixQpcc801AKRsld1uBwAsXrwY7777LlatWoWKigo89dRTqK6uxiWXXAKz2Yxp06bh8ccfx86dO7Fnzx7cc889mDNnDoqLi9X8iIQfsIa8wVoAGw0czN6mqeT0R2gJ1oMqQQGHP4bc3LclOgIDtWEOf+wYVNZFx/d2rJYFVIawv1dhrgEGvbQhxb4vwj+YZfoQBRw3C/OkY11Wrf06tkjSbomdgGrS6DgkJ/Jo7xSw8zDVyEYTqp+dS5Yswbhx43Dttddi2bJluOOOO3D22WcDAGbPno21a9cCkFz+/u///g8vvPACFixYgJ9++gmvvfYaMjIyAADLly/H2LFjcdNNN+Hqq69GXl4ennrqKdU+F+EfHkGUbdPTk4M/HZkzFfWiIrSEzzJduamWNfetpwyVXzCHv2klUhamqj46dv+PeQO/4TnhD6gMeg6jh5F9ejCE2oOqK3KGiqzTu8EyVGrZpkcSnY7DrIlSCcvGUqvKoyECQfWmHCaTCcuXL8fy5ct73HfgwIFuf1922WW47LLLen2dlJQUPP7442EZIxE+2jsFCALAcQipCDctSYfKOjda2mmRSWgH1uwxUUHJH+s91WYR4HAKiKMGkP3CMi4zx8VjU6kdja0CrHZBkUbL4cLpEuXeRiMikKECgJICI/YddWJ/hRPzZ2i/dkMrMMlfKD2oGIWsgWyNC4Igaq4eWA3sTgFOlzSPxkKGCpBkf59+24nNpTbccRmdB9FCbJydhGZhAVByIh+S/jw1STqVW0jyR2iIcEj+zCYOpjjp9RpaaQNhINiCtyjfCHO8dDyqNF5HVVXvgiACiSYOaSFk7gOhZASroyKZUSDUNUm/wSEZoWdPhmXr5X5Htc302wZ8TX31Osjz3mBnanE8EuM5NLV5sO8oZYyjBQqoCFUJ1ZCCQZI/QouwgEpJyR/HcT7ZHy26+sViFWSHsKGZOmSlSIFUZZ22JVVd5X7htEvvSkmBlB05VOmCm4rh/UbJDJVOx8kZyfLjtJAGfA5/KWZdxH4LamM0cDhlglf2t51kf9ECBVSEqrS0M0OK0E7F9CQKqAjtYbEpL/kDgCwypvALVj+VlszDFMcjK0WaH5jhg1apjKAhBSMvSw+ziYPTJZLLnJ94BBH1zcoFVABQkMuMKegYAECbxadiiSXmTJbc/jaVWsl5M0qIrTOU0BxKZaiY5C8WXP48gohlLzfikRcb4HAO/s8bzYRD8gcA2elkTOEP1Q3SojTXaxiQnSp9X1p3+qtQIaDieQ7FJPsLiKZWD9weSY6WmaqMYcJIVkdFxhQAfJK/lBgLqGaMjUecgUNtkweHq+hciAZi6wwlNAcLqNJClfyxDFUMmFJ8u9OGb36yYlOpDcv/2wxBoN0rrRIOyR9A1un+wjJUzIFNzlBpXvIXuR5UXSkZ4XX6o7oNv2Byv+x0PXQKGQcw6/Rysk4H4JP8JceIIQXDFMdj5rh4ACT7ixZi6wwlNAeT6IUcUMVQDdXKLzrkf3/9kxWvftKm4miI/pAb+yocUFFzX/9gAZUvQyX9XVXv1uxGhCCIqKyNnGV6V1gd1QGyTvcLXw8q5ey8mXV6Zb1bdreLZZjkLyUGLNNPRJb9UUAVFVBARagKq6EiyZ9/7DvqwK4jDuh1wA0XpQAAXv+0Heu2dqo8MqI3OlmGyqSs5C+HZaiaKUPVH8wyPTdLWqSmmQXodZItuVblkvUtHjhcIvQ6XyAYKUoKJMnf0RoXbPbBPZcqQS1z+FPwOGWm6mA2cRAE7df6RYJYzVABwCkTTNDrJJOaiho6F7RO7J2hhKbw1VApY0phd4qDeiGwcoOUnZo/IxFXnpuCX5ydDAD46xtN2HXYrubQiF6whqGxL+DLUNW3eKhguR9qTshQ6XjJfAHQrtMfG1detgG6EFpJBENGig5ZqToIInCwkrJUAyE39VXIkAKQXDwL86RMIRlTdG3qG3vLVbOJx7QSr+yPmvxqntg7QwlNwWqe0kJo6gsApnge8UZp8dE8SGV/NY1uWUt96RlJAKQs1ZzJJrjcwP+90IjjDXQB1hKssW+Cwi5/zDbd5hBlJ0GiOy63KEsiu2Z6hmVL/9bq7r9sSJET2ewUg8n+qI5qYGoVtEzvCpP9kXU60G7x2abHIiT7ix4ooCJUw+0R5d2n9JTQJ8vBLvt7/6sOCCIwrSQeo4ZJix6e57DkugwUDTeivVPA7//VAIt1cH7+aMQn+VN2qo0z8kjxSmBI9tc7tU1uCCIQH9e9OW5+DstQafN7k+unIujw1xUm+9tPdVQDomQPqq4UsoCKMlRo64xN23TGrEkm8DxwuMol14QS2iQ2z1BCE7R2eCCKAM8rM1kOZqc/i1XAp/+zAAAWn5nU7b54I48//SYTWak6HKtzY+lLDdSYUwN4BBF2h7cPlcK26QAZUwyEbEiRoe/WEJQFVFp1+vNlqFQKqGSnP7JO7w+3R0Sj97eneIbKK/mjgKprY9/YXK6mmHWYdJK0yUFZKm0Tm2cooQlavJmkVDOviOXsYHb6W7PZAptDRGGuAdPHxPe4PzNVj8duzUJ8HIefDjjwzDstVFujMja77/s3xSk/1TJjijqyTu8VVj81NKv7YjffG6hoNkOlkmU6o2i4ERwH1DV7BuVcqhT1LR4IImA0cCHXAJ8Ia+7b0OpBR4wrDmJd8gd0b/JLaBcKqAjVUKqpL2OwSv5cbhEffC2ZUVw6P6nbbntXRg0z4g+/ygDHSQHYe1929Po4IjJYvIYUBr206FIaylD1j+zwd4IDW763hqqpzYNOm7bmivZOj7zRpFaGKtHEy/VbB6iOqk9qG32W6X3NycFiNvFy8+5Y7kflcAqwO6WNqViV/AHA7EkmAMDecicaWrW5EURQQEWoiNIBFZP8DTZTiq+3WdHY6kF6Mo/50xP7feypExPwm4WpAIB/f9CKLTtpR0stWFNfs8L1U4zsdGru2x8n9qBimBN4uaaqql5bkqpj3vqprFQdTAo7QwaCr46KZH99Ea76KYbPmEJb52gkYXI/HR8e2XS0kJmqx7iRkgx0c6lN5dEQfUEBFaEassOfwgHVYKqhEkURK79oBwAsmJfkV6bj0jOScOFsM0QR+PMrTThM9seqYLWHp6kvgzn91TcPnvNdSXw9qHouePOzpcUqC2C0gk/up052iuGro6K5oy/C5fDHKMwl6/Q2i68HldJZwGiDZH/ahwIqQjWavU19lQuoBp/kb/tBBw5XuRBn4HDhHLNfz+E4DndcnoZpJfGwO0Q89HwDGkkmEHGYw19CmHZWs701VA2UoeqBKIq+Gqpemq6ygKVSYxkq2ZBCpfophmydXuGkWsw+kAOqMDVfHplHTn+yIUVi7NZPMVhAtfOQA62DTIUzWKCAilCNFoWa+jIGoynFyg1SdurcUxMDKsrV6zg8ckMmRgzRo7HVgz/8uxE2x+AJNKMBVp+jtGU6g2WoGlo9EARa9HaluV2AwyWC54Cc9F4yVBq1Tj+mssMfY2SeEQa9tKAlq+beYQF7+DJULKCK3aC2zRLbluldGZqpx0n5BggisGUnyf60CJ2lhGoo1dSXMdgkfxU1LmzdYwfHAZeenjTwE07AnMDjz7dmI8XM4+AxJx5/tYkW3hGESf4SwyT5y0jRgecAt8fnmElIVHsbXGen62DQ98wQyk5/GmvuywI8tSV/Bj0n97o7QP2oeqXOK7UdmhGe7El+jgE6Hui0iaiPUeOZWLdMPxGS/WmboM7SDz/8EN988w0AYP/+/bjwwgsxdepUPPTQQ3A6afIl/CNcLn8WmwiXO/oDh5VfStmpWRNNyMsOboGVm6nHozdnwaAHNu+w4eWPWhUcIdEfLEMVLsmfTschw9sQm4wpulPThyEFg2Woqhrc8Ghkk8Hp8skU1Q6oAKqj6g+HU0BTW3h6UDEMek4O/GPVmKLNG1BRhkpi7hQpoPppvx2WGLfT1yIBn6UrVqzAQw89hL179wIAli5dipaWFlx22WXYsGEDnnnmGcUHSQxO2K66UjVUSQk8dN4zOto1xs3tHqzf2gkAWHxmckivNX5UHO7/ZQYA4O31HVi7xRLy+IiBkWuowiT5AyBbK5MxRXeq+6mfAqRFsEEvBTFa+e6ON7ggiECiSfm+RsHgc/qjgOpE6rwZI1McF9bFfqG3jqosRq3TmeQvlntQdWX4EANGDDXA7QG+3UWyP60R8EywcuVK3HDDDbjllltQVVWF0tJS3HrrrViyZAnuu+8+fPLJJ+EYJ6EQDqeAI1XqT85Olyg3LFRq8cDzHFKZ7C/KJVAfb+yAyw2MKTDKdqmhcObMRFx9nhSY/f2tZmw/YA/5NYn+sXozVOYw2l8zYwrKUHXH5/DXe6ZHx3PIy2INfrWx+1/hdRwcnmPQhKMZM6Y4dMwJt0cbWTytwHpQDc3Qh/VYydbpMWpMQZK/nsyZLPWkItmf9gj4LK2qqsLcuXMBAN988w04jsMZZ5wBABg5ciSampqUHSGhKM+/34obH6vFd7vV3d1gxhF6nZRZUgrm9BfNxhQOp4CPNkpZpMvOTFbsgn3dBSk4fVoCPAKw9KVGzSwkByudzDY9nBkqZp0eozUWfdFXD6quMCe9Yxr5HVTWasMynTEsS49EEweHS8TRGF3Q9wXrQZUTJrkfozDGe1HJtukk+ZOZ662j+mGvnYymNEbAZ2l6ejoaGxsBSAHVyJEjMWTIEADAgQMHkJmZqewICcUQRVF2h1E7Q9HVkELJHT7Z6S+KjSnWbe1Em0XAkAwd5ng7pCsBx3H43dXpGFtoRIdVwEP/apAlFYTyWMNsmw4AWWSd3ityDVUvPagYrBeVVpz+jmmkBxWD5zmUjCDZX2/UNbEMVXilaCPzpCzhsTpXTGYJyTa9J6OGGTA0Uw+HS8T3e0hpoiUCDqhOP/10/PWvf8XDDz+MjRs34qKLLgIAvPLKK3j66adx5plnKj5IQhnqmj1yIW2ZyjteShtSMNKiXPInCCJWftEBAFh0ehJ0OmUX43FGHn+8OQs56Tocb3Bj6UuNg8LAQ4swUwozZagiitUuyL//vmqogC7W6Rpx+pN7UOWo24OqKz5jCofKI9EWNU1eQ4ow9aBi5KTrkBDPwe3RjjQ1ksi26ST5k+E4jmR/GiXgs3TJkiU49dRT8cMPP+AXv/gFrr/+egDA22+/jXnz5uHuu+9WeoyEQuw67Lsoql3k6mvqq+xEmSo3943OBeZ3u22oqncj0cThvFP9a+QbKOnJOjx2axYS4jnsOOTA395sjtk+J+FElvyFs4bK22OpvlkbWRYtwLJTyYl8v8FsvtzcV/3vThBEVGnEMr0rxSN8DX4JH6yGakgvPc6UhOM4FAz1GlPEoOzPl6GigKorzO3vu902OF107dYKAc8GcXFx+OMf/9jj9o8//hhxcXGKDIoID7vLfAFVS7uA1g6PbOIQaViNU9gyVFEq+WPZqQtnm8O6EC/MNeLhX2fioX814PPvOpGfo8eV56SE7f1ikUhI/liGqqVDgMst9tpzKdY43jCw3A/w9aJqavOg0yaErQGzP9S3eGB3itDr+s+qRRpmTHG02gWbQ4Apjha2AFDb3L+LpJKMzDNib7kz5owpnC4RNocULCSTy183SkYYkZGiQ1ObB9v22/GzCcqVBhDBE9TsWFlZiSNHjgAA2tvb8eijj+Kuu+7CqlWrlBwboTC7j3SXbZSpOEEzyZ9SlukMuYYqCiV/Byoc2HHIAR0PXHJa4I18A2XmOBNuuywNAPDyR23YuJ3kA0rCJH/hauwLSO5XRgMHUQQaW6NzE0FpBupBxTCbeNlhVG05FXv/vCw99ArLfEMhM1WPzFQdBBE4VElZKgCw2QXZLCHcphRAF2OKGAuo2jql+YzngcQwbkpFIzxPsj8tEvCV/ptvvsF5552H9957DwDwyCOP4O2330ZdXR2WLFmClStXKj5IInQ6rII8IY8fJWUSy46rd4FsCVsNFd/t9aOJd73ZqdOnJ8hmA+HmktOSsGCeJC18/NUmHKigWgmlsHolf+HMfHAc56ujItkfgIF7UHWFZamOqWxMcUxjDn9doQa/3WHZqaSE/iWlSuGzTo+t77/d4pP78TwFVCfCZH//22mLScMSLRLwbPD8889j9uzZuO2229De3o7169fjpptuwocffoibbroJ//3vf8MxTiJE9njlfsOy9ZhaLAVUalqxtrQr29SXIUv+osy9rq7ZjW9+knaaFs8PrZFvoNx2aRpmjouHwyXi98830MJcATweEXYnq6EK72Igi4wpulHtp+QPkHo+AepnqI516UGlNZjsjwIqCZYBHRJmhz8Ga+5b2+SRZcSxQFsnWab3x4RRcUgx82jvFLDjEG2EaoGAz9T9+/fj2muvhdlsxsaNG+HxeHDOOecAAGbNmoWKigrFB0mEDpP7jR8VJ1uxqlnk6nP5U3ayZAFaW4cAjxA9uzYffNUBQQCmFMdhdH7ojXwDQafj8H/XZ6Iw14DmdgG//3cDbDF04Q4HVofv3At3bQ419+2OPz2oGMOY05/aAZXGLNO7UlLArNNp0QZIgQ0ADImA3A8AkhN1yEiRrmuxJPtjhhRUP9U7Oh2HWd62KptIrq8JAr7Sx8XFwe2WLlibN29GRkYGSkpKAACNjY1ITo7s7jrhH7u8AdWEUXGyJvtojUu1oCNctumso7og+iZkrWOxCfhki7eRb4SzU4xEE48/35KFtCQeR6pc+Mc7LaqMY7DA6qfiDFzYa2LIOt2HxyOirtn/gIoFMGr3ovJJ/rRjSMEoGi5t8NQ2eaLWPVVJaptYhipyx2pkXuzVUcmW6ZSh6hPW5HfzDiuEKNpAHqwEfKZOnToVK1aswCeffILPP/8cZ599NgBg9+7deO655zB16lTFB0mEhtMl4oDX9nb8qDjkZukRZ+DgcImyPCaS2ByC7N6TprDLoF7HyRNwtFz8126xwGoXMWKIHjPHxqs2jiEZejxyo9SY+4sfOtHYShmPYGEBVYIp/Np/Zp1OzX2BuhYPBAEw6CHv6vcHq6Gqqldvc6m90yOb6ORrUPJnNvFybyyyTwdqVAio2CaomnXPkUa2TKceVH0ypTgeiSYOze0C9pbHzrmhVQI+Ux966CHU1tbivvvuQ15eHm655RYAwM033wyn04nf/va3ig+SCI1DlU44XSJSzTyGZeuh4329LdTY8WKLhzgDF5b6Etnpr137GSq3R8QHX0lmFJfOT1a9+Hbi6HiMHxUHjwB8sqVT1bFEM6zWIZwOfwyfKUV0bCCEk+oGaT7LzdT79VvKSdfBoAdcbqnxuRqw7FhWqi6srRJCQZb9UYNfOUM1NJIZqhh0+mNOismJJPnrC4Oeky3TyaVXfQKevfPz87F27Vps3rwZa9asQVZWFgDgn//8J9auXYvhw4crPkgiNJjcb9yoOHCctMhgha5q7Hi1dKmfYuNRkjTvjlZLFGSovvnJivoWD9KSeJw1M1Ht4QAALp4ruf6t2Wwh96Ag6bSFv6kvI4tqqGRqAnD4AwAdz2FYtrrGFFp2+GPIxhSUofJJ/iLYL6zQW/dcXu2KmSbs7V7JHzX17R8m+9tUao3oubFuayeeXpUqq5+IIPtQcRyHtrY2vPXWW3jxxRexcuVKJCUlwWiMbDE94R97utRPMUbmqdd9PVw9qBi+XlTaDqhEUZQb+V48LwlGgzasYedMTkBaEo+mNg8277CpPZyopJNlqCIh+fNmqCw2MaZcwHpDdvgLYLGb75WzscAm0mi5forR1To9Vhb0vdFhFeTNkpz0yGVORgwxgOckGVxTm7ava0pBkj//mD42HvFGDnXNHhyqDP8cJooi3vy8DX95vRXHmwx4ZU172N8zWgh4BhdFEY888ghWrlzZbWLlOA6XXHIJHnvsMUUHSISGKIrYXeZz+GOMzPXteEWasAdUci8qbS8udx5y4OAxJ4wGDhfNMas9HBmjgcP5s8x447N2fPRNB06bmqD2kKIOFthEIkOVEM/DbOJgsYmob/GgYGjsLkBkh78s/7M9Ut2SDVUqGVNUsIBKg/VTjJF5Rhj00iK3pskTUMA6mGDZqbQkHqa4yP3OjAYOedl6VNa5UV7tQmbq4P/+fZK/2J3P/CHeyGPmuHhs3G7Dxu1W2UQmHAiCiBc+bJU3ggFg234HjtW6NJ1hjxQBn6kvv/wy3n//fdx555344osvsHPnTmzYsAG33347Pv74Y7z66qthGCYRLJX1brRZBBgNHE7qYsfNMlTVjW7YHJENPGTJn8KGFIzUpOjIULFGvueckiiPWStcONsMngN2HHLEXENJJehkTX0jVBPDrNNj3ZgiEMt0xnC5ua86GSpWQ5Wv4QWJ0cBh1DDp+nEghuuofD2oIh/Q+IwpYqOOqr3TK/kj2/QBYU1+N24Pn+zP7RHx5P9rloOpmy9Jxtjh0lzw4Tcd/T01Zgj4av/ee+/hhhtuwC233IK8vDwYjUYMGzYMt912G2644Qa8++674RgnESS7D0sn/JgCIwx6n/woNUmHtGQeoijZp0cSX1Pf8Cw2WeZLyy5/x+pc+HaXDRwHXHpGktrD6UF2uh6nTpSKXT/eaFF5NNEHc/mLhOQPALK98iO1jBW0gCiK8oLXn6a+jHwVe1E5Xb4xj9BwQAUAxSOojkoNy3TGyDz1VCVqQI19/efkcSYY9EBVvTss6zm7U8DDLzRg3dZO8Dzw4DXpuPSMJMwaK5UErPuuU77mxTIBn6k1NTU45ZRTer3v5JNPRlVVVciDIpSDGVKMHxnX4z4m+4v0jle4elAxokHy9753l+dnE0yatEoGgAXzpEBv3VaaLAMlkpI/wGdMEcsZqjaLAKtdBMcFtuBlv7/mdgGWCJ/nxxtcEEQgMZ5TvMm50ozpUkcVq6gZUMkZqhhQDLjcIqzeLD/VUA1MoonHtBKp5cqmUmXrnjusAn73bAO+222H0cDh0ZuzcPYpUonCSbkuDB+ih80h4vPvyBU44DM1Ly8PBw4c6PW+/fv3Iz09PeRBEcqxh9VPje4loGLNAiPs9MekeOGrodK25K+1w4PPt0qTz2XztZedYkwpjkN+jjRZrv+eJstAsEZY8pdDzX1luV9mii4gg5dEEy/3rIp0lupYrU/uFw7HUyVh1ukHjznhiVH3T19AFXkZGrNOr6hxDfrvnxlS8JzUB40YmK6yP6VobHXjnr/VYfcRB8wmDn+5I0u2aQcAjgMuniO5E6/6piPmmwsHfKZecMEFePbZZ/Hpp5/KWk1RFLF27Vo899xzOP/88xUfJBEcze0eVNW7wXHAuMKeAZVamuywZ6i6uPxp0ZHq400WOF0iiocbMbGXQFcrcByHi+dKAd9HGy2a/C61itzYNwx91nqDrNO7OPwFIPdjyLK/CDv9MYc/rcv9AGBYth6J8VJD+EjLxLVCTZN07VIjQzU0U494IweXGzjeMLh/521ey/SkRF713ozRws8mmMDz0nrueH3ov8+qehfu/GsdyqpdyEjR4el7czBhdHyPx511cgIS4zlU1bvx4z57yO8bzQQcUN14442YOHEi7rnnHkyYMAFz5szBhAkTcN9992H8+PG46667wjFOIgh2e+V+hUMNMCf0PNRMk10Wwd4Woiii2SvFC7fkz+X2mQNoBadLxEfeAs7LzkzS/K702ackIj6OQ0WNCzsOxW4xeqD4bNMjZUoh/ZYaYjhDFWgPqq4w2V9lhJ3+mBEGC+i0DM9zch3VvhiU/YmiiLqm4M+xUOF5DiOGMtnf4A5oZct0qp/ymxSzDlOKlJH9HTzmxF1/rUNtkwd5WXo8c1+OvF48EVMcj3NPlSSAH34d2+YUAZ+tRqMRr7zyCl544QVcd911OO2003Ddddfh3//+N1577TXExWl3xz3W2NOLXXpXRgzRR7y3hdUuwumSgpxwmVLEGXk5M8AcBbXC+u870dIhIDtdh3lTtG9HbjbxOGuGL6VP+IfVFmGXv3SWodJmVjYSBOPwxxjOelFFXPIXPRkqwCf7218Re5srrRYBdqdUo8dcNSMNk/0dHeR1VLJlOjn8BcScyZIcb2Np8LK/7QfsuPfpOrR0CBidb8Azv80ZcANhwVwzOA7YuseOKgWyY9FK0LPCvHnzMG/evG63NTQ0oLKyElOnTg15YEToyIYUfQRUcUYew7L1OBbB3hZM7pcQzyHeGL7FZmqSDla7G60dHs2YPgiCiJUbpCZ4i05Pgk6n7ewU46K5ZqzebMHmHTY0trpjogdKqLAMVUKEXP4yU3XgOCkD2mYRNGfDHwlCk/xFPkMlCGJUWKZ3hWWoDsSg0x+rn8oIsEZPSQrzYsM6XbZMpwxVQMyelIB/vNOC/UedqG92yxtt/rKp1Io/rWiEyw1MPikOj/4myy+VRV62ATPHxmPrHjtWfWPB7ZelBfsRohpFz9a1a9fiqquuUvIliSCxOwUcOiZd9PoKqACgMC+yTn/hrp9iyE5/Hdpxp/t+jx3H6txIjOdw/qnaaeQ7EKOGGTFhVBwEAVizmSzU/UGW/EUoQ2XQc/JvKlaNKULJULGA6niDC54IFVY3tHpgd4rQ8cGNWQ1KCnzW3ZHuX6g2tUxSqkL9FIM58w5263Rq6hsc6Sk62dF5847AZH+fbLFg2UtSMDV7kglP3J4dkGT9ktOkeuvPv7XAZo+tuYFBZ+sgZf9RJzwCkJWqQ05638HLSHnHKzI7jizASQvzDrrs9Kchyd+7X0jZqfNnmSNWW6MUF8+TAsBPtnTCPcgdppRAlvxF8DhnMae/5sFdsN4bDqdPthxMfUt2upR1cLl9mYhww+R+w7L10EdJtjorVY+MFB0EAThcGVtZqhrveZGjgsMfg2WoqhvdgzqglWuoyDI9YGYz2Z+fbn+iKOLNz9rw1zeaIYjA+bMS8ciNmQFnYaePicewbD067SLWbY1NV2A6WwcpXeV+/RkfME12pHa8mtuYZXp4Tz2tWacfqnSi9KADPA8sPF27Vul9MWdyAtKSeTS1ebA5BH12uPjv2jY8+M96uT5PTdweEQ7vOCLl8gf4jCliMUPFslOJJi6oXW0dz2FYFmvwG9mASiuSZH9hWapYa/Bb53X4UzNDlZakQ1oSD1GU7NMHK8zlLzkx9qTLoTJnslSbveuIQ1YE9YUgiHj+/Va8/HEbAOCqc5Jx35Xp0AXhrMjznNy7ctU3HTFZy0sB1SBl9wD1Uwwm+auodUUk89ASKclfsrYkf6x26rSpCcgJUNesBQx6DhfMkrJUH23UluxvxyE7Xl3Thu/32GUjFjWxdpE7RKqxL+ArlI9F6/Sucr9gnTNZHVOkelEd8wZu0WJIwYjVBr81Kjb17UphhDdB1aCNMlRBMyRDj+LhRogi8L+dfcv+3B4Ry//bhPe+lMymbr00Fb++ODUk5+FzTkmEKY5DRa0bPx1Q/1ocaVQ/Wx0OBx566CFMnz4ds2fPxooVK/p87Pr163HeeedhypQpuOKKK7Bnz55u97/66quYM2cOpkyZgoceegg2m7Ido6MFjyBir3dhOWGAgGpIug6mOEnqUlUf/oVYuJv6MrQk+Wto8eCrbVJWZ/GZySqPJngumG0GzwM7DjlQrhGXKY9HxLPvtMh/D7QjFwk6vXK/eCMXUSlXVgxbpzPL9Nys4IMTuRdVpDNUURZQyU5/R2NrwVSrlYCqS7uTwQqT/FENVXDMGUD2Z3cKePiFBqz/3gqeBx68NgOXnhH62iTRxOOcUyRX4A++ij1XYL9mhlWrVvn1Yjt37gx4AE8++SR2796N1157DdXV1XjggQeQm5uLc889t9vjDh06hPvuuw9//OMfMXXqVLz66qu4+eabsX79ephMJnz++ed47rnn8Je//AUZGRlYsmQJ/vKXv+Dhhx8OeEzRztFqFzrtIhLiOVlz3Rc8z6Ew14C95U6UHXeiYGh4L+6RMqVgLmetFvUzVKu+scAjAJNOikPR8N57OUQDWWl6zJpowqZSGz76xoK7r0hXe0j4aKOl28JCCwEVy1BFUu4HQM58xmSGqiH0/kDDvdK7YxFq7sss2odHQQ+qrhR5M1Q1TR60WTxIiQFra0EQUdesXg+qrsgy/QjVPasBM6WIhXMrHMyZkoCXP27D9gN2dFgFJHXpQ9phFfDQv+qxp8yJOAOHR27IxCkTTIq994LTkrDqGwu+221DdaM7agx3lMCvT/rggw/6/YKBpAutVitWrlyJl156CePGjcO4ceNw6NAhvPHGGz0Cqi1btmD06NFYsGABAODee+/FG2+8gcOHD2PChAn473//i2uvvRann346AGDZsmX49a9/jfvvvx8mk3InSzTA6qfGFsb5pYUdmWfE3nInyo+7gOnhHVtLmJv6MmTJn8oLbLuTwyf/kwo0L5sffbVTJ3LxvCRsKrVh/feduHFBqqrmGi0dHryyphWAZBve2OqJWD+1/pAt0yMo9wO6mlKo/x1EmlAc/hhyhioCfVQ6rII8Fw6PsgyV2cQjP0ePyjo39lc4cfK4wX99bWr3wOUGeF4yelKTWJD8Mdv0ZJL8BUV+jgGFuQaUV7vw7U4rzj5Fkus3trrxwHMNKK92wWzi8Nit2QOWhQTK8BwDpo+Jx4/77Pjomw7csih2LNT9uvp88cUXYXnz/fv3w+12Y8qUKfJt06ZNw7///W8IggCe9/2YUlNTcfjwYWzbtg1TpkzBBx98ALPZjOHDh8Pj8WDXrl24/fbb5cdPnjwZLpcL+/fv7/b6sQCrnxpI7sdgE3QkJAQsg8BszcOFVkwpfjgYj06biPwcPU4ZH/0LjylFcRieI/UuW/99p1yEqgYvr2pFp03ESfkGnDYtES+tapVNT9SkUwWHP8BXQ9XU5oHHI0ZNnzMlCKUHFYOZQ7S0C7BYBZgTwnf8WBYsM1UX8cBbCUpGGKWA6mhsBFTMkCI7Taf672rEUAM4TqoPbunwhN0xN9K4PaI8h1IfquCZM9mE8moXNpbacPYpZlTVu3D/M/Woa/YgI0WHJ+/IQmFueBQzl5yWhB/32fHp/yy47oIUmOJi4zj6dfXJy8sLy5s3NDQgLS0NRqPvoGZmZsLhcKC1tRXp6T5J0fnnn48vv/wSV155JXQ6HXiexwsvvICUlBS0tLTA4XAgOztbfrxer0dqaipqa2sDGpPHo/6CDPCNI5jxsAzVmAKDX88vGCpNyGXHnWH9/KIoygFOijm833WyZHQDq12E1eZGnDHyF0Gn041Ne6TFxsLTzBBFARo5vULiwjmJ+Od7bVj1TQcumGUKqYg1WPYddeLTb6XM3+2XpaC6UfpiG9s8ip9Xgf4WLVZpcZ8Qx0V0PklOEKHXAW4PUN/ilAOswY6nixxrSHrf3/lAxzHOAGSk8GhqE3C0xoExBeGT5x6tkebo4Tl6zVxzAqFouAHrvwf2ldvh8USup14o18VQON4gyeuGpOtUP15GPTA0Q4fqRg8OV9oxtThe1fEEQ3/HsdW76cpxgClOVP37jlZmTYzHf9e244e9Nmw/YMWj/2lGq0VAXpYOT9yWiSEZoZ3L/R3D6SUG5GZK5+j6rRb8fFZi0O+jNoF8R6pecW02W7dgCoD8t9PZXR/c0tKChoYGPPzww5g0aRLeeustLFmyBB9++KH82N5e68TXGYhdu3YF+jHCSqDjabHwaGjJAM+JcLXvQ2npwM+xOjgAmahr9uDb73fAZAyP25/VwcHtyQQAHCvbjeowbqyJIqDjM+EROPxv6y6kJUW+lmpHWRxaLMlIjBeQE3/Ir2MRDeSYOBj1GThW68b7n+7F6NzISk8EAXh2dSoAA6afZIezbR9aGgwAUlFd34nS0qqwvK+/v8UDh+IBJMHlaA/bWPoiOSEdzR06bPl+PwpzYqOWqsXCw+XOgI4XcfzobtQc6//x/R3HtIQUNLUZseWHI3C0hs904cediQASYNK1RvwcUQLeqQeQhj1lNmzfXopI76lE+jq9fXcCgEQY0IbS0uMRfe/eSEtMRnVjHLb8UAE+is23ejuOtS06AOkwGQXs2rkj8oMaJIgikJmchsZ2PX77jwaI4JCX4cINZzWitrIWtZXKvE9fv8Vpo0yobjTj7c8bkZtwKOJzhBqoGlDFxcX1CHjY3/Hx3XddnnrqKRQVFeGqq64CADz66KM477zz8P777+PSSy/t9tyurxVo/dSECROg06mfQmcyxkDH8+WPVgAtOCnfiJkzJvv9vKw1tWho9SAps0TutK00Ut+MeiQlcJg+zf+xBUv6h7VoaPFgaH6J3DslkryxsQGAEwvmJWHG9PyIv384Obu8FWs2d2JvzRBcen5GRN977ZZOVDW2IiGew++uK0Basg5pOS68+Gk9rA4DJk+erOj7BfpbPNDYAaAdQ3PSMHnySEXHMhB53zSgucOJ1MyRmOztRzLYKT3oANCIIRl6TJ06uc/H+XMcxxxoxeGaTnBxuZg8OSU8Awbw/tYmAHZMGTcEkydHLsOjFGNdIp7/pBqddh5D8sdHzKgh2OtiqHyxpwWAFWOLsjF58uiIvW9fTD7ejj0VHXBy2Zg8OfpqVPo7jjsPS7/n9BSj4nN5rHHGsTa8u8ECERwmn2TE0huHKiZFH+i3OLpIwPrttaht0YM3j8Gkk8Kzrgw37HP6g6oBVU5ODlpaWuB2u6HXS0NpaGhAfHw8kpO7Wzju2bMHV199tfw3z/MoKSlBdXU1UlNTERcXh8bGRowaNQoA4Ha70draiqysrIDGpNPpNBFQMQIdz95yKVswfnR8QM8rzDOgodWDihoPJp0Uns/f2imNLT05Mt9xWpIODS0etFsR8WPq8YjYXyF93tOnJWrqnFKCS05LwprNndiy047mDhFZqZGZSto7PfjPaqmn168uSEFmmhQoZ6VL218WmwiPwAfc5d0f/P0t2uzS/5MS+Igf95x0PXbBicZWcdCdc31R2yxln/OyDH595v6O44ihRgCdqKr3hPX7Y9bsBblxUXmcTDpg1DAjDlQ4cbjKjWE5kV0sRfo6Xes1ehmaadTE8Ro1LA5AB8pr3JoYT7D0dhw7vE7fyYmRnz8HGz+flYTPvrVi5th4/PaXGRG9LqYk6XDWyYlYvcmCVd90YmrJ4N/gU7VSbMyYMdDr9SjtooXatm0bJkyY0M2QAgCys7Nx5MiRbreVl5dj2LBh4HkeEyZMwLZt2+T7SktLodfrUVJSEtbPoDV2BWhIwWBWrGXHwyffapENKSIzSTLjCzWc/sqrXbA7RcQbBdk9bDBRmGvExNFxEATgk82Ra/T7yuo2tHcKKMw1dDPEMJs4GLxfs9rW6bLLnwoOiLHY3Jf1oFIiS+Jz+gvf9+d0ifKYo80yvSss678vBhr8sh5UQzO0scBn1+uKGhcEITwSfbVo7yTLdKXIzzHgwyfz8NCvMsMSTA3EgnlS9v1/O23yb2gwo2pAZTKZsGDBAixduhQ7d+7Ehg0bsGLFClxzzTUApGyV3S5t9y5evBjvvvsuVq1ahYqKCjz11FOorq7GJZdcAgC48sor8Z///AcbNmzAzp07sXTpUixevDimLNMtNkG2Ug1UtjfS2ywwnFasssNfSoQCqmT1nP72eZte5me6wfthXR+NXDxXmizXbLbA5Q7/Rf1QpROrN0nB2x2L07q5bXEcJ1vxq22dbrVL34Ua7m3ZEWju63BKLnhaQQmHPwZz+jte74LHE55z+niDC4IIJMZzyIjQXBgOSrz9qPZXDO6AyuMRUe/9PQ3RSE+dvCw9DHrA7hRRM8gWqu0WZlwVG85w4UYN0yhGYa4RU4rjIIjAx5sit/GqFgHPDna7Hc8//zy++uor2Gw2CEL3CyvHcdiwYYPfr7dkyRIsXboU1157LcxmM+644w6cffbZAIDZs2fj8ccfx8KFC3H++eejs7MTL7zwAmprazFmzBi89tpryMiQ6jd+/vOf4/jx43j44YfhdDpx9tln4/777w/040U1e8scEEVpYZEe4IXaZ53uhCiKYfkRtkSoqS8jVbZOj/zij+3aDs8avL1CZk9OQHpyC5rbBWwuteL06eFz8hFFEc+80wxBBE6fnoDJRT2drTJSdKhrVr8XVadNOt/MKmao6sKQofIIItZuseA/H7dBFIE3/pgbVmtxf1GiBxUjO02HOAMHh0tEbZMbednK94g6ViuNN3+IQdXFTqiUjJA27Q4dcw5qm/6GVg8EATDogYwIXbsGQqfjMGKoAYcrXSg/7kJeVnT1MuuPNm+GKpks0wcFC09LwvYDDqzdYsG15ycjzjh4j2vAV6A///nPeO+99zBz5kyMGTOmhzQvUEwmE5YvX47ly5f3uO/AgQPd/r7ssstw2WWX9flaN910E2666aaQxhPN7C4LTu4HSM0ldbzUQ6e+xYOcdOV34lhgE+4eVAxZ8qdKhsobUGUPrt3Drhj0HC6YbcZ/17bjo42WsAZU67+3Yk+ZE/FxHH5zSWqvj2GBumYkf/GRX2BmhSlDta/cgWfeacGBY75sxM7Ddpw6UX1dPJPPKZGh4nkOw3L0OFLlwrG6MAVUddImy/Cc6F4E5+fokRDPwWoXcbTGhVHDIm/8EwmYVCknXa8ptcHIXKMUUFW7MHuy2qNRDpL8DS5OmWDCkAwdaps8+PJHK847NfpMePwl4CvQunXrcM8998R04KJVdh+WAqpgOl8b9ByGD5E6a5cfd4UloGqOcIaKvU+ka6g6bYLcuHMwZ6gA4OezzXj9s3bsPOxA2XGnLB1Vkk6bgBc+bAEAXH1eCrL66K/E5FNqB1RqSv7Y77bNIsDuFBAf4m5ga4cHL3/UirX/k3p+JcZzyE7Xo7zahd1lTtUDqg6rgA6v/HBIhjJzVn62AUeqXKisc+FnE5SXjMtzwxBtyMeChec5FI8wYvsBB/ZXOAdtQFXTJaDSEj5VyeC6xrR5JX+UoRoc6HgOF81NwosftuLDrztw7s8Sozoz3x8Bn7EulwsTJ04Mx1iIEHB7RDkrEqzt+cg8ZkwRHk18pAMqtSR/ByqcEEWpCaTZNLgKhk8kK1WP2ZOkRedHG8Ojkf7v2ja0tAsYlq3HotOT+nwck7k2q15DJZ1viSpkqBJNHExx0vuGkqXyCCJWfdOBa5ZWy8HUOack4rVHcnHpGdIx2HMkfH2a/KW6gTmH8jDFKbMAY4EOyyQpjRxQRXmGCvDJ/vYfVf9cCBe1CpqeKAm7XpeH6XqtFnKGigKqQcP5pyYizsDhcJVLNk4bjAR8xs6ePRsbN24Mx1iIEDhU6YTDJSI5kQ965zPcO16yy1+EAiom+WuNsOSPGVKo0ftKDS6eKy2w13/fCYtN2eC1vNqJ97/qACAZUfTnVJShEVMKVkOlVL+PQOA4KYMEBB9Q7T7iwC1P1OKZd1pgsYkYPcyAZ+7LwQPXZCA9RYdx3gz4/goHnC51Nwx8hhTKBSfMmIJZmyuJIIjy6w4fOggCKu8cd2AQG1Mwyd8QjTj8Mdj1uqrBrfrvUEnaLN4aKjKlGDQkJ+owf6akZvjw68FrThHwyvv888/HI488gubmZkyaNKlXF70FCxYoMTYiAHYf8cn9gk2nMrlWOKzTPYKIVm+mKFIZKha4tXcKES2aZplC5oI12JlcFIcRQw2oqHFh/dZOXHJa31mkQBBFEc+tbIEgALMmmTBjbP/yK9nlT/UaKvUkf4BkrFBR4wrYmKK53YOXVrXi8++kjJTZxOH6i1Jx4RwzdF1qR/Kz9Ugx82izCDhc5cTYQvUaNtYoaEjBYAFVVRgyVA2tHtidInS8smNWCzbHlVW7FJGYapHaJq/Dn0KSUqXISNEhOZFHe6eAiloXTsofHNcbJvlLSdRWAEuExiXzkrB2Syc2lVrR0OLuU7ofzQT8ie6++24AwKpVq7Bq1aoe93McRwGVCuxi9VNByv0AX2+LyjoXnC5R0b4FbRYBgghwHJAaoZ2n5EQePAcIItBqESJiUSyKIvaVswyVAa72sL+l6nAch4vnmvHMOy34eGMHFswzK6KR/ma7DdsPOGA0cLh1UdqAj9eC5M/lFuXdYjUyVEDgxhQej4iPNlrwyppWdNqksZ9/aiJuuDhVls12heM4jBsZh//ttGH3EYeqAVV1GORY+dnSa7V0SPVZSQo6GTK5X16WHvpB4IqXmapDRooOTW0eHK50BVW/q3Vq5B5U2loAchyHwlwDdhxyoPy4c1AEVB6PCIt3DiLb9MHFqGFGTDopDjsOOfDxJgt+fVGq2kNSnIBniC+++CIc4yBCQBRF7CkL3pCCkZWmQ6KJQ6dNRGWdsq5NTO6XkshHLFOk4zmkmHm0dAho7fBEJKCqa/agpUOAXgeMHmbEvr1hf0tNcNbMRLy0qhUVtW5sP+jA1OKetuaBYHMIeP49yYjiF2cl+bVgZse3tUOARxC7ZVUiBaufAtRx+QOAnACa++44ZMez77TIMt+i4UbcdXkaxgwQJI3vElAtPjP0MQeLLPlTMKAyxfPITNWhsdWDyjqXogGjz5Ai+uV+gLSoLxlhxJadNuw76hh0AZXTJcoSYq30oOpKfo4UULGNhWinvUt/OyU3MghtcMlpSdhxyIFPNltw9XkpqjQbDicBn7F5eXnyf+np6TAajcjOzu52OxFZjje40dIhwKCXFkTBwnFc2GR/kTakYKQlRdb5jWWnRuUZEWccXJNFfySaeJw1U7JN/3hjR8iv9+Zn7Who9WBIhg5XnJ3s13NSk3wZyTYVeo8BPrlffBynSkAH+DJU9c19n/NNbR489koj7vl7PcqqXUhO5HHPFWn45+9yBgymAN/GzZ4yB0RRvfqNagUt07syPMdrTFGr7Dx4jNVPDZKACvDVUQ3GBr/1LW6IIhBv5CKmrAgEttHENhainXZv/VRSQuQ2XonIMWuiCdlpOrRaBHy9rVPt4ShOUDPEjz/+iMWLF2PatGmYO3cuJk6ciMsvvxzfffed0uMj/IC5ppSMiAs54meyv/JqZS+Ocg+qCAdUqXIvqsgssOX6qcLol18EysXzpP4Sm3fY0BBCY9mqehfe/ULSSt66KM3vRoA6npOPt1p1VFZmSKFS/RSALqYUPY+B2yNi5RftuHZZNTb8YAXHARfONuO1R4biwjlJfgeBRcONMOil35VaizmnS0RDq3SclXZgC5cxReUgy1ABQEkBc/obfAEVq9HLydBr0uqZZWYHS4aqrZMs0wczOh2Hi+ZI64QPvraouhkXDgI+a3/66Sdcd9116OjowK233opHHnkEt9xyC1pbW3HDDTdg+/bt4Rgn0Q97joQu92P4rNPDk6FKS47sRMkCuEg5/TGHvzEFg0v64g+FuZJGWhCANZuDc/JhRhQuNzBjbDxmTQqsD1C6yk5/ajb1ZWR7M1R1LZ5uF6zSg3bc+Fgtnn+/FVa7iJICI/71uxzcc2V6wE00jQZOrtlgDcUjTW2zN3sQxyneLJwFVEpbp1fITX21Jx8LlmKvKqKm0S0bCgwW6rxZ3iHp2jRIYBsJrM4r2mEZKqqfGrz8fLYZBj1w8JgTe8sH1yZMwGft008/jenTp2PNmjW4/fbb8Ytf/AJ33nknPv30U8yYMQPPPvtsOMZJ9MMuBQOqwlyfa5OStKgs+YtEc1+3R8ShSul7GxMjlukncvE8yeFvzRYLXO7Ad5++3WXD93vs0OuA2y5LC3hXWG1jCjUt0xlZqdJ3YHdIBd4NrW48uqIR9z5dj4oaF1LMPH57VTqe+20OikcEP2ew+Wa3Sn1FutZPKZ09yPcGPEo6/XVYBbS0C97XHzwZKnMCL39fg80+nWWotFg/BfgyVC3tAmwOdWTOStLm7UFFGarBS4pZhzOmS+UBH34denmAlgj4rN21axeuueYa6HTdF8Y8z+OXv/wldu7cqdjgiIFp7fDIspRxI0NfxLPeFo2tHrR3KrcolXtQ9eIaFk4iKfkrOy65IyYl8BiWrc0LcLiZPcmEjBQdWtoFbC61BvRcp0vEP1dKRhSXzk8OqvEp60UVqZq5E7F6a6jUlPzFGXm53uPlj1px7bIafPWjFTwnyTJfe2Qozp9lBh9ijRdzFN1dps4iOhyGFAwmyTve4IbHo4wshdVjZabqVA24w0HxiMFZR1XbrE2HP4Y5gZfNG2oGgexPtkwPMGNORBestco3P1lV7xupJAHP6omJiXC7e//hut3uQaeJ1DrM3a9gqAHJCvRtSDTxcgPDcgVlf6qZUkRQ8uezSzdqUm8fCfQ6Dj+fJe0+rdoYmOzvnfXtqGmS3BivPtc/I4oTYRkqtWqotCD5A3zGFKs3WWB3iBg30ojnHxyCuy5PV2SeACA3+K2ocSm6+eIvsp11GAKqrFQd4gwc3B7l5FTHBqHcj1EygtVRqZOtDBe1LEOl0YAK8G0oDIaAqp0yVDFB0XAjxo+Kg0cAVm8aPFmqgM/aqVOn4sUXX4TNZut2u9VqxYsvvojp06crNjhiYJSU+zGY098RRQMqb1PfCFiXd0V2+YtEQOUtyo5VuR/jgtlm6HipN1rZcf92rGub3Hjzc8mI4pZFqTAFmeHJIMkfAGCYN7uXlsTjgWvS8Y97cxTvU5OWpJMzsXtVyFLJGSqFHf4AgOc5WcamlNPfYDSkYLA5b/9R56DaVK1t0n5ANXQQGVO0sRoqCqgGPZd4TazWbA6uPECLBDxL3HfffVi4cCHmz5+P0047DVlZWWhoaMDXX38Nu92OP//5z+EYJ9EHu8MQUBXmGvC/nTZFnf5aOpjkL8KmFEzy1x5+yR8zpCiJQUOKrmSm6jF7cgK++cmKj76x4J4r0wd8zr8/aIHDJWLSSXE4fVpC0O+ttikFk/wlqCj5A4DfXJKKKUXxOG1qAsxh7OcybmQcqurd2F3mwCkTAjMQCRXZMj1M9S35OQYcrnIp5vRXUTv4LNMZo4YZoddJDdTrmj2aDkD8xe4UZKl4OLKgSjGYAio5Q0WSv0HPnCkJyEhpRVObBxu3WzF/RqLaQwqZgK+0I0aMwDvvvIOZM2fim2++wX/+8x988803mDlzJt59912UlJSEY5xELzicAg4ek4KeCUpmqHKVdfpze0R550ktU4rWDk9Yd047rIK88Ir1DBUAXDxX2n1a/0MnLLb+g9kf99mwcbsNPA/csThwI4qupKtcQ8Ukf4mqS/70uGC2OazBFKCeMYUoirLEKRwZKsBnTFGpkDFFpSz5G3wBldHAYVTe4Kqjqm2S5pDEeA5mk3Yl3Oz8HwySP1ZDRZK/wY++q4X6V4ND9hfUlWj06NF4+umnFR4KESj7K5xweySZE6t7UgIm+SuvcUEQxJCL11l2iucjP1GmegMqjyAFPUrVj5wIqx3IzdJTQS2ASSfFoWCoAUdrXFj3XScWnp7U6+NcbhHPvSsZUSyYlySfe8EiS/7aBYiiGPFaNtaHSu0MVaRgxhTSXCRCH6FmnE1tHjhdIngeyEkPX4YKUKYXldMlyhLF4UO0m+0IhZICIw4cc2L/UQdOmxp8llkr1HZx+NNyTezQQVhDRbbpscHPZ5vx+mdt2HdUmjeiXd3j18y+atUqzJs3D2lpaVi1atWAj1+wYEGIwyL8oavcT8kJf1i2Hga9ZLtc2+wJWVLD5HZpSbqQg7NAMRo4JJo4dNpEtLSHL6Ci+qnucByHi+ea8Y93WvDxxg5ccpq513P0g686cKzOjbQkHtf9PCXk92UBldMlotMmwpwQ2fOtk7n8DTIXt77Iz9EjOZFHe6eAQ5XOiPVfY/KmnDRd2II4Js1TohfV8QYXBFEyK8mIcB1ppJCd/gZJg1/m8DckTAG7UrDrc22TGx5B9Ls5txYhU4rYIj1Zh9OmJmD991Z8+HUHllwXAwHVgw8+iHfffRdpaWl48MEH+30sx3EUUEUIFlApKfcDpG7WI4YacLjShbIqZ8gBlVpNfRlpSTp02txo7fBgxNDwyG2Yw18sNvTti7NOTsRLH7XiWJ0b2w84MLUkvtv9ja1u/HdtGwDgxgWpisjTjAZJnmOxiWhq84Rd8nYiLEOltuQvUvA8h7GFRny3247dRxyRC6hkQ4rwyeeY4UabRUB7pyekzRiW5Ro+xKDpbEcosN3lg8ec8HhE6CKUrQwXWu9BxchK00HHAy430NTqQbbGA8C+8AgiOqwsQzU4Nx2InlxyWhLWf2/F1z9Z8ZtFnoi31lESv355X3zxBbKysuR/E+ojCCJ2lylvSMEYmWvE4UoXyqtdmD05tNdSq6kvIy1Zh6p6tyw9VBpRFH0ZqkLKUDES4nmcdXIiPvrGgo82dvQIqF78sBU2h4ixhUacfbJyBanpyTpYbG40tYcvgO4L2TY9RjJUgDT/fLfbjj1lDlw2PzLvWRNmQwoAMMXxyE7Tob5F6vU3bmTw81dF7eCtn2Lk5+iREM/BahdRUesKWb6rNnUa70HF0PEchmTocbzBjZpGd9QGVBarAFbmTBmq2KGkIA5jCozYd9SJNZstuPq80JUqauHXWZuXlwejUZocf/jhByQkJCAvL6/Hf0ajEWvXrg3rgAmJozUudNpExMdxGJWn/EV6pPc1j/hpe90fajX1ZTCnv+YwOf1VN7rR3inAoIdcmE1IsKLTLTttaGjxafx3HLJjww9WcBxw5+XpikpB01W0Tu/UQGPfSNPVmCJSltlM8hdu9zVWRxWq7E+2TB+EPagYOp5D0fDBI/tjQXuOgvXJ4WIwOP0x46pEExexWkxCG7BGvx9vtMCtUCN1NQj4qr9kyRJUVlb2et++ffvwzDPPhDwoYmCY3G9cYVxYpBWFXqe/8urQ6wfUaurLkJ3+LOFZYLPFw+hhRhgNdCHoSmGuEZNPioMgAKs3S41+PR4Rz3qNKC6YZZYXYUrBalTUsE6XJX8adgVTmuLhkmV2c7uAmqbIfOfh7EHVFZ/TX2gL1YpB3IOqK0z2Nxic/pjLn9YzVIAvUxvNAZXPkEL7ASyhLPOmJiAtmUdTmwebS61qDydo/JopbrrpJhw5cgSAJG+67bbb5IxVV5qamjB8+HBlR0j0ihxQjQxPRoTJNY7Xu+FwCogzBr/jLjf1VauGyhvItYTJSttXP0XZqd64eF4SSg858MkWKZ2/ZrMFZcddSE7kcf1Fyqf31bROlyV/MZShijPyOClfkmzsPuIIqwyPEe4eVAzZ6S+E5r6CIHaroRrMlDBjiorI2ugrjcUmyPU80dBTazA4/ZFleuxi0HO4cLYZ/13bjg++tuC0adHZk8qvmeI3v/kNVq5cCQD48MMPMXbsWKSnd2/WyfM8kpOTsXDhQuVHSfSA1U9NGB0/wCODIz2ZR4qZR5tFQEWtO6QsgtzUV6UMVarXgpU1aVQaX/0UGVL0xqxJJmSk6NDU5sHqTRa8uqYVAHD9hSlh2Y30WadHNqByukS4vOuZWJL8AZLsb99RJ/YccShaD9cbnTZBlgeFW/LHAqBQelE1tnpgd4rQ8eHPqKkN21QqO+4KeSNOTeqapB9yciIfFZsjg0HyJ2eoKKCKSS6ck4Q3PmvH7iMOHKp04qT86Nug9mt2nzp1KqZOnSr/feuttyI/Pz9sgyL6p6HFjdomD3g+fFkRjuNQmGtA6UEHyo47QwqoVJf8Jfua+yqN0yXicJUUUJVQhqpX9DoOF8w247VP2vDP91ogisDofAN+Ptsclvdj51mkJX9Wuy9gN8WIyx9j3Mg4rPyiQ97oCSdsFz7FzIfdnj7f6/R3vMEddJ8tJvfLy9IP+tqQzFQd0pN5NLcLOFTpCothUiRg51g0yP0A6dwCoj1DRZbpsUxGig7zpibgyx8lC/XfXZ2h9pACJuAz9/HHH+8zmLJardi4cWPIgyL6h8n9RuUZwrp7xmR/ZcdDq6NS3eUvKXySvyPHnXC5pcVdJKRO0coFs83Q8ZBdnO5cnB62filqmVIwuZ8pjovqXjDBwBbOR2tcsFjDkwlmREruB0gBQryRg0cIfrHK5H75g1zuB0gbcayOat/R6JX9sR5U0WBIAfhkiW0WAZ228P7+wgWT/FENVezCzCm++KFTPh+iiYBX49XV1bjxxhsxadIkjBkzptt/06ZNw8033xyOcRJdkOV+Yd79G6mAMYXTJcJik1bRakn+WP+rcEj+9pV7s1MjjIO2v4wSZKToMHdqAgDg7JMTw7pzncEyVBGW/Fm9Dn/RIBFSmvRkHXKz9BBFYE95eBfSckAVAfkcz3MY5jWmCNbpj2WoRsRAQAX4rkvb9ttVHknw1EZZhirRJEn0AanBbzRCTX2JsYVGFA03wuUGPtliUXs4ARPwmfvYY4/hp59+wmWXXYYxY8Zg6tSpuP7661FcXAyO4/Dcc8+FY5xEF3Z5M1Tjw1Q/xWDW6WUhWKczuZ9BD5hVcj5jGSq7U4TNoWxQxXZhqX5qYO66PA33XpmOu69IC+v7sBqqTpsIhzNyu7VsZ1it81xtxo/02aeHk5qGyFimM2RjiiCd/mLBMr0rM8ZK16UdBx1wuqLTArm2Wbpuab2pb1fY7+F4Q3QGVG2yyx8FVLEKx3G45DSpFODjjRZ4osxCPeAz94cffsA999yDP/zhD1i4cCHi4uJw//334/3338eMGTOo8W+YsdoFlFVJF+jxYXL4YxTkGsBxUmYn2AJ/2ZAiSadaBscUxyHOa2eudJZKNqSg+qkBSU7U4YLZZsSHuVA90cTJ9vXh6j3WG7Ho8NcVlnXcE+Y6qkhK/gBfM95gnf5YZmuwO/wxCnMNyEjRweES5c2/aINlqKLB4Y+RG+VOf2SbTgDA6dMSkZbEo77Fg/qW6JL9BXzl7+zsRHFxMQBg5MiR2Lt3LwBAp9PhyiuvxHfffafsCIlu7C13QBCBoRk6ZKaGd7KPN/JysWuwsj8WiKkl9wOkXQ9Z9qegDKzN4pH74bC6AUJ9OI6TLfojaUxhtcWu5A/wtXDYf9QZ1uaM1Q3SXBQpxzy5F1V94AtVi1WQg3qW6RrscByH6WOkLNUPe20qjyZwRFFETVN0Sf6A6LdOJ9t0AgCMBg7Lb8/Gw7/OiJgKQSkCPnOzs7PR2NgIABgxYgTa2trQ0NAAAEhNTUVTU5OyIyS6weQ0kXJPYg1+g5X9tcg9qNTddUpNUt7pj2Wn8nP0SEqgi4CWUKMXFXP5i6Wmvl0ZMcQAs4mD3SniSFV4Gru6PSLqvLuWkc5QHQsiQ8WyUxkpurA7EmoJJvv7cW/01VG1dwqwOaQNgZz06MmWRHtz33aS/BFeRucbo7IXVcBn7rx58/D0009j+/btyMvLw5AhQ7BixQpYLBa8//77yMnJCcc4CS+7VAuogstQtcgZKnUnSdnpT0HJ335WP0XZKc3B6qgimaFiNVSx1oOKwfMcxoW5jqqu2Q1BkHYxI7VJk+e1Tm/vFAJ2nvIZUkTXTmuoTCuJB8cBZdUuNLRG1wKfmTqkJ/NR1UcrmiV/giCiQzaliJ4gliC6EvBsceeddyI5ORn/+Mc/AAD33HMPXnvtNcyYMQOrV6/Gr371K8UHSUi4PaKcFYlUQMWs00OV/KmdoQqH5I/qp7RLugrNfTuZy18MZSJOhM1Lu8vCk6Gq7mJIwUfImt4UxyPbm6kI1JiC1V3FgmV6V1LMOhSPkObFH/dFV5aqpslrSBFFcj/AJ/mrbXLDI0RXMb/FJoANmSR/RLQS8IyRlpaGlStXor6+HgBw0UUXITc3F6WlpZg4cSJmzpyp+CAJiSNVTtgdIswmLmIWvMzp72iNCx5BDLi/jmYCKjPLUCmzwBZFEftZQEUOf5qDWadHsheVLPmLsaa+Xenq9CeKouJGNJE2pGDkZxtQ3+xBZV1gzWqPeQOwWLFM78qMMfHYf9SJH/facd7PwtPEOxywDFW0BVQZqToY9IDLDTS0eKJq/Kypb0I8B4M+dudPIroJeisgOztb/vf06dNxww03UDAVZrrK/SK1Ozs0U494IwenSwzKjpVJ7JjkTi2YKYZSkr+qejc6rAKMBk4OOgntIEv+IpmhYpK/GM5QFRcYoeMlqWVds/LffU0Ee1B1ZbhXshdoHdUx2TI99uaIGWNNAKR+VNGUMYm2HlQMHc/JQVS01VGxgCqFslNEFOPXjLFkyZKAXvTxxx8PajBE/0TakAKQJukRQw04UOFE+XFXwAsDX4ZK7RoqZSV/TO53Ur4Beh3tqGmNNBUyVJ0x3NiXEW/kMTrfiAMVTuw+4lB8l1yW/EV4sSv3ogrA6c/pEuWFbX6M1VABkhQ60cShvVPAwWPOqKk1rW32ZqiizGEMkDZAK+vc0sZDsdqj8Z/2Tq/DH1mmE1GMXzPG1q1bu/1dX18Pt9uN3NxcZGVlobW1FZWVlTAajSgpKQnLQGMdURRVCagAYFSeFFCVHXdi3tSEgJ6rFclfapKykr995WRIoWXUyFCR5E9i/Kg4OaA6c6ayTk3VamWo5Oa+/meojje4IAiSjCkzJfYWijodh2kl8di43YYf99qjZq6Mxh5UDFZHVR1lzX3lpr6UoSKiGL9mjC+//FL+9+rVq/HUU0/h2WefxcSJE+XbDx8+jFtvvRXnnXee8qMkUN3oRnO7AIMeKBkR2QuT7PQXoDGFzS7A7rWfVbMPVdf3b1VI8keGFNqG1VC1dQhB1f4FQ6w39mWMHxmH97/swG6FG/yKoqhaQMV6UVU3uOH2iH5lpZmBRX6OQbWm5mozfYwJG7fb8P1eG64+P0Xt4QyIKIqobWamFNEXBEer01+7V/KXTJbpRBQT8Nn797//Hffee2+3YAoARo8ejbvvvhsvv/yyYoMjfOzxZqeKhhthNET24syc/gK1Tm/2ZoPijRxMceouKJjkr8MqwOUOTc/vcApynx0ypNAmKUk8eA4QROWC6IHo9Db2jeUaKsCXQS+vdsFiU+67b7VIGzQcBwxJj2xAlZmqQ3wcB4/gf33KMdkyPfbqpxisH9W+o05YrJH5HYZCS7sAp0s6x7LToi9DFa0BVRtZphODgICv/C0tLUhOTu71Pr1eD6vVGvKgiJ7IhhQjI7+AZ6YLNY1u2Oz+XxRZU9+0ZJ3qO7RJCTx479keanPfQ5UueAQpSIumxo+xhI7n5KxkpHpRWe0+p6pYJiNFh6EZOoiiTxqrBEzGlJWqi/imEsdxyPf2o6r005jCZ0gRfQtzpchJ12N4jh6CAPx0QPv26TVNvnMsGt3mhkZpc19WQ0VNfYloJuCzd/LkyXj++efR1tbW7fb6+no8++yzOPnkkxUbHOFDrp8aHfmAKsWsk00lymv8z1Kx+imWHVITnucUa+67jzX0LYxTPVAk+ob1HouUMYWvhkr9811txnmzVHsUlP2xgCrSlumM4UNYHZWfGSom+YvhDBUATPe6/f2w16bySAYmWi3TGSyg6rAK6IiCjCCDufxRDyoimgl41njggQdw9dVX4/TTT8eUKVOQmpqKpqYmbN++HSkpKXj++efDMc6Yps3iQUWtNNGrkaECJNlfc7sdZcddGOunzK1FI4YUjLQkHk1tnpCNKfZT/VRUkJGsw2G4ImJM4XSJcHnX2bEu+QOkeWrD91Z5I0gJWPYg0vVTDOb0d8wPYwpBEOXHxbLkD5Bkfx981YEf9tnD0ptMSaI9oDLF8UhL5tHSLqCm0Y2k4dFxjWI1VCnk8kdEMQFf+UtKSrBmzRpcfvnlsFgs2L17N+x2O66//np8/PHHGDZsWDjGGdOwXd7hOXrVJhxmTFFe7fT7OSxw0UpAJTv9hbjAljNUUeJaFaswp7/mCARUnV2ksGrXC2oBVke196gTHo8yPYhky3S1MlRe6Z4/Tn+NrR7YHSJ0vHoBoFaYdFIcDHqgvtmDY7XalqLVyAGVNq5ZwcBaCkRTHZVsm04ZKiKKCWqmz8nJwQMPPKD0WIg+UFPuxxjlraMKxJiiuUsNlRZg0sNQTApaOjyobfKA44DiEdGx+xerpKdErheV1earn4pU020tUzDUgEQTh06biCPHXShSYKdcdvhTKaCSe1H5Iflj2am8LH3M96mLN/KYODoe2/bb8cM+G0YM1W7Grq5JmivUCtqVIDdTj73lzqiqo6LGvsRgwK9ZY9WqVZg3bx7S0tKwatWqAR+/YMGCEIdFdGV3mZQVmhDh/lNdKfQ6/ZVXu/yWbWilqS+DBXahSP5Ykf3wIQaSdmmcjAiaUlBT3+7wPIdxhXH4fq8du484lAmoGqQgRa2MT162HhwHtHcKaLN4+lULsExMrNdPMWaMlQKqH/facekZvZtaaQE5QxVhF0klYb+PaMlQCYKIdiuT/NH8SUQvfs0aDz74IN59912kpaXhwQcf7PexHMdRQKUgTpeIAxXqOfwxRgwxgOelxURjmwdZqQOfOi2yKYU2MlRKSP6o/1T0wALoiEj+vBkqCrJ9jBslBVR7yhxYeHpSSK9ldwpyxjs3S50gJd7IIztNh7pmD47VujBhdH8BFXP4o4AKkAKqf38A7DjkgNMlRtyl0R88goj6Zm9AFcUZqqFRZp3eaRcheEUjZJtORDN+zRpffPEFsrKy5H8TkeNgpRMut+RYpqYW32iQbIMrat0oP+7yK6CSM1Qp2pgk072Sv1Bc/siQInpgNVSRyFD5HP60t1BUC7YBpIQxBVscmk0ckhLUC1rzcwyoa/agss6NCaP7fpyvB1X0LsyVpGCoAZmpOjS2erDzsB3Tx5jUHlIPmlo9cHsAHS/1HYtWZOv0hsD6RqpFu0Wan01xnCYDbYLwF79m+7y8vF7/TYSf3Ud8cj+13ZEKc42oqHWj7LgLM8f1f0EURbGL5E8bF6dQJX+CIGI/GVJEDV1NKcLtLkaSv56UFBjB80BDqwd1zW7khCCjki3TVcpOMYbn6PHjvoGd/tj9JPmT4DgO08fE47NvO/HDXm0GVEzul52uhy6K6yBZjWFdiwduj6j5Gj5fU1+aO4noxq8r3JIlS/x+QY7j8NhjjwU9IKI7e7z1U+NUlPsxRuYZ8PVPQJkfTn+ddp+NtBb6UAFdJH9BBlTH6tzotIuIN3Ky6yGhXVgg73IDFpuIpIQwBlQk+euBKY7H6GFGHDzmxO4jjtACKpUNKRj+GFNYrD55Ikn+fMwc6wuoblmk9mh6wizTh0axwx8gzXtGAwenS0R9i0f138xAtJFlOjFI8OuXtnXrVr9fUO0symBCEH2W6WoaUjAK85h1+sBSApadSoznEGfUxiKTBXZtHQIEQQzYjY3ZpZ803Aidxnf9CEmmmpTAo8MqoKnNE1apGEn+emf8SG9AVebA/BmJQb8OC6jUdl/Ll5v79j0HsuxURoqOAuwuTC2JB88BR2tcaGhxIytNWwv9Wq/DX7T2oGLwPIehGTpU1LpR3eDSfEBFlunEYMGvX9qXX34ZtgE4HA4sW7YM69atQ3x8PK6//npcf/31PR539dVX4/vvv+9x+8KFC/H444+jra0NM2fO7HZfampqQMGg1mho1aHDKmVERuerX7Mzyuv0V1HjGlBK0OKtW9GKZTrgy1AJomSukRqgWcb+cqqfijbSk6WAqrnNg4Iw2jWT5K93xo+KwwdfW7AnxDqqmgZ1m/oy8r29qKob3XC5RRj0PedA2ZCC6qe6kZyoQ/EII/YddeLHfXacd6pZ7SF1I9qb+nZlaKZU7xwNxhS+DBXNnUR0o+jMYbVa8eOPP2Lu3Ll+P+fJJ5/E7t278dprr6G6uhoPPPAAcnNzce6553Z73LPPPguXy7cruGPHDtx999248sorAQCHDx9Gamoq1qxZIz+G56P7B1peJy0ASwqMmtBB56TrkBDPwWoXUVnnQmFu34EFk9VpKaDS6zgkJ/Jo7xTQ0uEJOKCihr7RR3qKtFMbbmOKrn2oCB/jvJn1suMuWO1C0AGnViR/mSk6mOI42BwiahrdGN5LjZQvoCK534nMGBuPfUed+GGv9gKqGo1kQZUgN4qc/to7qQcVMTgIeOY4fvw4li5diu+//x5OZ++1NPv27fPrtaxWK1auXImXXnoJ48aNw7hx43Do0CG88cYbPQKq1NRU+d8ejwd///vfccMNN2DChAkAgLKyMhQWFspuhIOBo96ASgtyP0CScxbmGrCnzImy4/0HVKyGQCuGFIy0JBZQCSgM4Hl2p4Ayr9RxbCFlqKIFuRdVmK3TO+1UQ9UbWal65KRLVuP7jjoxrSQ+4NfwCKKcPVA7Q8VxHPJzDDh4zIljta7eAypvfRXVT/Vk+hgT/ru2Hdv22+ERRE2ZP9R6LdNDqfXTCrLTXxQEVG1el79kqqEiopyAr/6PP/44fvrpJ1x22WUYM2YMpk6diuuvvx7FxcXgOA7PPfec36+1f/9+uN1uTJkyRb5t2rRp2LFjBwShb2vrDz74AG1tbbjxxhvl2w4fPoyCgoJAP46mYRmq8RoJqABgZK6vwW9/aK2pLyMtyF5UB485IQhSXYTWtP9E3zDL/lB6j/mDbEpBkr8esPkrWPv0hhbJzlqv04ad9XCv7K+yvvfFqs8ynQKqExlTYITZxKHDKuBAxcDmRpHC7RHR2CLNEYMiQ+V1w4yGgIoyVMRgIeCZ44cffsA999yDX/7yl3j99dfx5Zdf4v7778e9996L66+/Hl988QXmz5/v12s1NDQgLS0NRqNvxz8zMxMOhwOtra1IT0/v8RxRFPHyyy/jmmuuQWKir8j5yJEjcLvduPTSS1FXV4fp06djyZIlyM7ODujzeTzh71njD/XNTjR36MBzQPFwvWbGVTBUWtAcqXL0O6bmNmkiTzHzmhk74NNpN7e5AxrX3jI7AKBkhMHv57HHaenzxxppSdIOeGNrYMe7K/4cR5ahio+j430iYwsM+OIHYNcROzyewGVeVXXSwntIhh4QBQT79Sr1e8zLlubAihpnj9dyuUV5EZuXpa25TytMKY7DplI7vt9tRfHwwJYg4ZpTD1c6IYiA0QAkJ4hRf9xy0qR5r7rBDbfbrTmzsK7HsdWboTKbaO6MJmJlfRPI5ws4oOrs7ERxcTEAYOTIkXJGSqfT4corr8Ty5cv9fi2bzdYtmAIg/92XnHDr1q2ora3F4sWLu91eVlaG9PR0LFmyBKIo4u9//zt+85vfYOXKldDp/N/V3LVrl9+PDSc7y40AUjAkzYVDB3aqPRwZt9UAIBUHjlpRWlra5+OOViUDiENn63GUltojNbwBcTsSASTgwJEalKZ1+v2870qlz5NibERpaWVA76mVcyoW6WiJA5CMY9VtKC09FtJr9XccG5vTAOhRe7zs/7d35/FRVff/+F93ZpLJJCF7CASQVUISErKBoEGECvoDUVzqp1IBt9IFoVo/LmC1UK0I2I+i9lOhSsHWXxcFXKrWykcRNxSjJIRNdsIWsu+Tycyc7x937k0mmSQzySRzZ+b1fDx4tLmz5Izn3pvznvM+74O9kn9sqNlfDC16AHHYf6wJ3367F54ubf36UBiAAYgMbejynuOu3l6Ptib53nz4RA327j3l9FhplR52exyMIXaUnNiHM9oax2pCUqTcn58UVCIj+XiP3sPb99TXP40EYMK4Ic0oKir06nv7grxlSSIazQJffFWEiDDh6ya5tG/fPlwsl++dZaUnsHcv753+huObVh4HVAMHDkR5eTkAYPjw4aipqUFZWRkSExMRExODiooKt9/LaDR2CJyUn8PCXOfaf/DBB7jyyiud1lQBwLvvvgtJktTXPf/888jPz0dhYSFycnLcblNGRoZHAVhf+fxIFYBG5KZFIytrhK+boxrdaMcf3z2P6gY9xozNRGQnpaht/7kIoAUT0kcga7zn6yb6yv7SOnx+oBahpgRkZV3q9uvWbrsAwIYZl4/AhEvdS8G02WzYt2+fZs6poBTejP9/ZzkstnBkZXmyaq6VO/0o3pTPj4z0S7nGrp0Mu8CGf59Ho1mHqIFpGDPUs/8+BadrANQjZVQcsrJG9bgd3roeoxJb8NePLqKyPhQTJkxw+vb/071NACoxYrAR2dlZPf4dgSx5uBVvfFaKkrIQjB6b6dF2Bn1xT61tsGHvqxcAAHfOG4Lxo3t+jmlJ/JvnUVFjR8LgVKQM19Y9qW0/Wv55EYAd2ZmXenxvIN8JlvGN8jnd4XFANW3aNDz33HMYNGgQsrOzMWjQIGzatAlLlizB1q1bkZSU5PZ7JSUloaqqClarFQaD3JSysjKEhYUhKirK5Ws+/fRT3HvvvR2Om0zOO6/Hx8cjJiYGpaWlHnw6eaZNCyfH/hPyNzUZY4yaaI8iZoAeA2P1uFhlw+lSGzLGuF4nUFUnp0DFxxg01f64KPk8q663u92u8moryqps0ElA6ogw6PWefcWulXMqGCXGyudnZa2t133QVT82OsqmD4jQ1vmuBXo9kDbSiG8OmnHwpBUpw03dv6iN8xXyvSQ5McQr/217ez1eMkiCJAF1jQL1TZJTtdAzF22O53inrYFocIIewwfJZb0Lj7RgWk64x+/hzXvqv3c3wNICjBkWgsxLTZpLj+up5IQQVNQ0o7TSjrRR2jwXdTqduoYqLorXjD/i+KaVx6sAly1bhqioKKxfvx4AcP/992PLli2YOHEi3nnnHdx5551uv1dqaioMBoNTGkdBQQEyMjJcljyvrKxESUkJcnNznY7X19dj4sSJ2L17t3qstLQUVVVVGDXK/75tajLbceysHFCNH6WdghSKkcnyIPV4J4Up7HahFgHQXJU/R5EMJeBzx8GT8qzpiMEhMLHogF9RilI0mAXMFvf73BNCCG7s243eFKY4VybfZ3xd4U9hDNUhKU4+r0632+BX+ZkFKbqWlyYH1XsONPm0HTabwFuf1AEAbrpqQMAEU4B/VPprNAvYHLdlbuxL/s6tM3jBggV4++230dzcjNjYWLz++utYu3YtAOD666/Hq6++igceeABbtmzBbbfd5vYvN5lMmDdvHlauXImioiLs2LEDmzZtwsKFCwHIs1Vmc+v6myNHjsBoNGLo0KFO7xMZGYnc3FysXr0aRUVF2L9/P+6//35MnTpVXe/lTw6elCvKxUTYkBirrYAEAEY5Nvg9ftZ1QFXXaFdvkp7u9dTX4pQqf3XuLzRUAqpxTOXyOxFhEowh8iBJKeXvbZYWAavjdGLZdNeUL4aKj3sWUAkhNLMHVVtKSfSSUufB6ukL8s/DGFB1aVKanAa+54AZQvhufc/nRU24WGVDdKQOM/Iiun+BHxnsB3tRKZv6hoVKMIby3kn+za0zuLq6Gg899BDy8/OxatUqHDhwwKl6Xl5eHu655x5MmjTJ4wYsX74c6enpWLRoEVatWoWlS5di1qxZAID8/Hy899576nMrKioQFRXl8lukNWvWIC0tDYsXL8aCBQswZMgQPPPMMx63RwssVvkPTOow7ZSVbWvUEHmw0FnpdKVkelSEDiEGbX3jF9OmbLq7f8gPneCGvv5KkiS1dH9lH23u2+BI95MkeWBAHY0bEQqdBFystKGsyv0BXl2jHQ1N8n9fLZWzHuoIqJQS6YAc/CkzVNyDqmuZY4wIDZFQVm3DqQu+G/Bv3ynPTl2XH4nQkMC6dpP9YIZKSfeLimQwRf7Prb9Q77zzDvbv34/t27fjvffew9///nekpKTghz/8IebOndvpeid3mEwmrFmzxmV1wMOHDzv9PHv2bMyePdvl+0RHR2P16tU9boeWTB5vwv8+lIiq0gO+bopLakB11gIhRIcAt8oxExCrsXQ/oDXlr8UqpxtEmLr+I2qzCxw+LQe2qSM4Q+WP4qL1OF9hQ0UfBVRKul+4UYJOQxuVakl4mA6jhobgaEkLio81Y3qee8HRuTJ5MBgfrUeYhr7BVveiapPyV1Zlg7lZQK8DhgzUTvCnRcZQHTLHyOvq9hxowojB/R+AHjtjQeGRZuh0wPVXel7OX+v8YoZKCaiY7kcBwO2zOD09Hb/+9a+xa9cuvPjiixg2bBiefvppTJ06FQ888IDT+iXqvUuHhSJUo3+ThyWFwKCXv5kvrew4SFXS6WIHaO8mGRaqg8koD3rdSfs7db4FTc0CJqOE4T74o0+9F+9YR9VXAZW6qS/T/brUk7Q/Lab7AfI9EHBO+VNmp5ITDTDoGVh3Z6Ij7e+bg77ZVkOZnboyKxyJMdo6v7xBuWbKqmxosWqzbHptvbKpr/a+fCXylMcjAIPBgB/84Ad44YUX8Nlnn+Ghhx7CmTNncMcdd2DmzJl46aWX+qKdpCEGvYRLHGsEjp/tmJZYqdGCFIq2aX/dUdZPpQwPhZ6zD35JOQ/d6e+eUFL+wlmwpEs9KUyhzFBppSCFQrn/na+wwtIi97+yforpfu7JS5UDqsIjzWjuo4Ixnampt2HHnkYAwE3TB/Tr7+4vsVE6hIVKsAugtFKbs1RM+aNA0quzODo6Gj/+8Y/xj3/8A3/5y1+g1+vV6n8U2EYld76OSlmrotWASpk5c6fS38GTXD/l79QZqj4KqBodM1ThrPDXJWWG6tjZFjSZ3RtAK+lKWlo/BQBxUTqEh0mw21tn0ZT1VJewIIVbRgwOQWKMHpYWgaKjnld/7I13P6+HpUXg0mEhSB8VmKnckiRpPu2vpkG+J0cz5Y8CQK/O4rKyMmzevBm33HILFi5cCIvFgl/84hfeahtp2Eil0p+LgEoJVLS4hgpobZc7KX+HTnD9lL9TAvu+Kkqhlkxnyl+XBsYZMDBWD7sdOHjKvYI7Wk35kySpTdqffA9UC1IM0lZbtUqSJDXtb8+B/kv7k0ul1wOQZ6cCqVR6e2rp9DJtBlTKDFV0pDbHCkSe8PjO39DQgP/85z9455138NVXX0Gv1+Pqq6/G/fffj8svvzygb07USilM4ap0emvKnzYHmHFupvw1me04eV7+fKkjOUPlr+L6eIaq3lGFLoIpf91KH23ExW8asf9YM3JSwrp9vlZT/gBgWJIBh09ZWgMqzlB5LC/NhPe+aMCeflxH9VlhE8qqbYiJ1GF6bmCVSm9P65X+almUggKIW3+lrFYrPvnkE7zzzjvYuXMnzGYzUlNTsXz5csydOxfR0dF93U7SGCXlr6S0BZYW4VRyVvtrqOSbd3U3KX+HT1tgF8DAWL2aNkb+J76fZqjCu6kYSXLa38ffNLpVmMLSIlDu6DOtzVABzoUp6hvt6j5nw7iGym2548Kgk+TiPxcrrRgY1/f9rJZKnxp4pdLb03rKX+sMFQMq8n9u3b2uuOIK1NbWIioqCjfffDNuvvlmpKWl9XXbSMMSYvQYEK5DXaMdpy+0YMyw1pS41ip/2gxC3E35O+DYf2oc0/38mjJDVV1vh80moPdyBTY15Y8zVN1SClMcON4Mm110WejlQoUVQgAmo6TJAdclbVL+lFmq+Gg9Ipn66bYB4TqMGxGKAycs+OagGbOv6Nvy5UdLLCg62gy9Dpg7NfBKpbc3WOMzVMrGvpyhokDgVkCVnp6Om2++GTNnzkRoKAeXJOe/j0wOQdHRZhw/1xpQ2ewCNY6ZH63OUCmBXndFKZQKfyxI4d9iInXQ6QC7XQ6iE7xcIlmp8sc1VN0blRwCk1FCg1ng5LkWjB7a+d+Ttul+WkwlH+bYi+r0hRacusD1Uz2VlxqGAycs+PpAU58HVGqp9JzALJXenpIqe77c6nLPSF/jGioKJG6NADZt2oQ5c+YwmCInreuoWheY19TZYReATgKiNbgPFdCa8tfVGiohBA4pAdVInvf+TKeT1CBaScvypgZW+XObXi8hbaR7+1FptSCFYujAEEiSvIZOqVLHkumem5RuAgB8e8gMm63v9kuSS6U3AABuuiowS6W3NyjOAEkCmpqFOhukFUJwDRUFFp7F1GOjHJX+2pZOV9LooiN1mt23SUn5q+4i5a+syoaKGht0OmDsJQyo/J1SIKUvNvdlyp9nlDLV+7vZj0rrAVVoiIRBcfK95Mt9TQBYkKInUoaHYkC4DvVNAofcrP7YE+9+Vo8Wq3w/TwuSL8lCQyQkOFKetZb219wiweq4HWsxpZfIUzyLqcdcVfpTClJotWQ60Frlr8Es1E0521PS/UYlhyAslJeJv1P3ouqDgIob+3pG3eC3uxmqMvm+orU9qNoa5giglG//GVB5Tq+TkDNOKZ/e1Ce/w2oTeGuXo1T6VZGaS33rS23T/rSksVnug9AQiX9jKSDwLKYeGzFYHjxU1NhQUy8PVKs0XuEPACJMEkIcY7TKTtL+uKFvYFEKU3TW372hpPxFsMqfW1JHGKGTgAsVNpRXdz7IUwaAWiyZrmhf0e+SJO22VcsmpsoB1Td9VD5dKZUeO0CHqwK8VHp7Wi1M0WCWh5/c1JcCBc9k6rHwMB0Gx8sDVSXtT1mjEqvR9VOAXFAjJrLrtL+DXD8VUPqydDpT/jwTYdJhpGN2u/i46xQvu120pvwlanfWp20AZTJKSIjR7hdJWqZs8HvopAW1Dd6/RtVS6fmBXyq9Pa1u7tvgmKFiuh8FCp7J1CvKOiol7U/re1ApWkund1yoa7MJHDnNCn+BpC83921kyp/Hxo9ypP11so6qotaGFiug08n7wGlV2xmqS5JCgiqVzJsSYw0YMTgEdiEXp/CmIyUW7AuiUuntJWt0LyplhooFKShQ8EymXlG+aT5+Tg5A1D2oNB5QdVXp78S5FpgtAhEmSS2NTP4tro9mqIQQTPnrAWUd1f5O1lEp36YnxRlg8PK+Yd7UtqofS6b3jjJLteeAdwMqZXZqWk6417dM8AdaDagalZQ/lkynAMGAinrFb2eoBnSe8qesnxo33AidRisVkmfi+2iGqrlFwOaY5GTKn/vSHTNUR0ssMFs6zhJrvcKfIjZKhwhHuXwWpOidPMc6qj0HzRDCO+XTq+ts+D+lVPr04CiV3p6S8ldeY+u0CJMvKCl/nKGiQMEzmXplVLI8iDh5rgV2u1DXUGk+oFJmLFyk/B04Ic+2jRvB9VOBQjkfq2ptXhusAUBjk/xekgSEGRl8uyspTo+EGD1sdqj7vbXVdlNfLZMkSf1SSflf6pnMMUaEhkgor7bh5PmW7l/ghnc/l0ulpwwPRWqQ3s+jI3UwGSUIAVyo0M4slVqUgmuoKEDwTKZeGZJoQGiIBLNF4Hy5VU2hi43S9qmlFM2ocjFDdUit8Becf4ADkRJQtViBukbvbXDZYG7d1JfrZ9wnSVKX66iU9CQtl0xX3HdbLH75o1hMSg/zdVP8mjFUhwmXyueEN9L+nEulDwja61OSJPWLCS1V+mtUZ6i0/eUrkbu0PeolzdPrJQx3rB34/rRF3flc8zNUnaT81TfZcbpU/qOTOpIFKQJFaIikppZ4cy+q1vVTvJV6qqv9qPwl5Q8ARiaH4oYrB2h2I3N/oqb9eSGg+mxvI8odpdKn5YT3+v382eB47a2j4gwVBRqeydRrSqrLt4flP4J6HTAgXNunllrlr9Z5tuLwKQuEAAbF69WgiwKDmuZZ670ZKqXCH9dPeU4JqA4cb4bd7pyGqaT8DdF4yh9516Q0EwCg6KjZ5do6T2zbKc9OzZ0afKXS29PiXlSNzazyR4GFZzL12ihHpb8CR7nbmAF6zRdziIl0nfJ38AQ39A1U8VF9MEPVJuWPPDN6SAjCjBLqmwROXWhdM1PfZFdnuv0h5Y+855JBBgyM1aPFChQdcV0B0h3fn7ag+JhSKj04i1G0paT8aWuGStmHil9cUmBgQEW9NtJRmOJChVLhT/unlTJbUdtgh83W+u24sqEvC1IEHmUvKm+WTlc39WXKn8f0ekldp9h2HZUy6IuJ1HFvryAjSRLy0lqr/fWUUir9qtxwtcJnMNNa6XQhBGeoKODwTKZea1/dSuvrpwA5b1uSACGAmnp5UCyEaFOQgjNUgSZeTfnz5hoqpvz1hqvCFP5S4Y/6xsRUOe3vmwNNPXp9VZ0NH33jKJV+FWengNaZ3vPlVq9WOe0ps0XAanPMUDGgogDBM5l6LS5Kr6bQAdrf1BcA9DpJvZEraX+llTZU1dmh1wGXDuOeMoEmrg/2omLKX++0FqZoLZ3uTwUpyPtyxoVBJwGnLlhRWun5jMq7n8ml0seNCGVhIYekOAN0khzItF837AvKl5ghBm43QYGDARV5hbKOCvCPGSqgTWEKR0ClrJ8aMzQUxlBeGoFGOS+9mvLXpARUPF96InWkEZIkf3Ou9IuSlsQZquA0IFynplx7Wu2vfal0koUYJCTGyvc/LRSmUNZIRkXogracPQUejgLIK0a2Sfvzm4BK2YvK8Y2dun5qJNdPBSJlLYV3i1LI6TORXEPVI5EmHUYOlr+MUcqnnyuTC1SwIEXwmpTuSPs76Fna36ffNaKixoa4KJZKb09Llf5qHAEV0/0okPBsJq9oO0OlBCpaFzOg3QwV108FNLUoBVP+NCXdkfa3XwmomPIX9CY69qMqOGR2KhrUnW2OYhRzpw5AiIHXZFtaqvRXp8xQscIfBRD/GPmS5o1K9t+Uv+o6G1qsAt+flmeoUlnhLyApRSkazQJNzd5ZR6Cm/HGGqsfaFqZosQpcrJQD3uRErmMMVmOHhyIqQoeGJqFmDnTn+9MW7D9ugUEPzM2P7OMW+p9kDW3uq6yhYoU/CiQ8m8krhg8OgbL1VKyflKlVU/7q7Dh+1oIWq5y/P3QgvxkPROFhEsJC5ZPUW7NU3Ni395TCFEdKLCgpbYFdAMYQyS+2X6C+oddJyElxlE93s9qfWio9J1ydjaZWWkr5q2XKHwUgns3kFWGhOiycE41rJkdgmJ8EJLFKyl+tzWn/KS6SDUySJKmzkt4qTNHQxH2oemtQvB7x0XpYbcDHBY0A5MEfr8PgNtGD/ajalkq/cTqLUbgyWEN7UdU0cIaKAo9/jHzJLyycHe3rJngkts0aKqXCH9P9Alt8tB7ny62o8FLpYK6h6j1JkpA+KhS7vmvCh1/Lg2JW+CNlg9/Dpyyoqbchuov1Nv9ylEpPHRHKNbCdUK6pihobzBY7wnxYybZWSfmLZEBFgYNnMwWt2KjWlL/WGSr+MQ5kShqZt2aomPLnHUran7p+igUpgl5ijAEjk0MgBPDtoc5nqaw2gbeVUumcnerUgHAdIkzyFz8XKrxXmKcnajlDRQGIZzMFLaXKX2WtDWcuymkQnKEKbN4snS6EUGeomPLXO0phCgVLphMA5KV2n/a3q02p9CuzWSq9M5Ikta6jcmxN4Cssm06BiGczBS0l5c/uyP5KTjR0mVZC/k+p9OeNohRmi1DPHab89c6YYaFqwRCAKX8kU9dRHTBDCNfl07d9LBejuP5KlkrvTrJG1lHVcYaKAhDPZgpaoSGSmgIBcHYqGMR6cS8qJd1Pp4NTMECeM+gljBveev0x5Y8AIHNMGIwhEipqbDhxruOsyqGTzThwQi6Vfh1LpXdLKwFVTYN8/2VARYGEZzMFNWWWCuCGvsFAmaHyRsqfmu4XpmNFOi9QNviVJGBQPAMqkr/0mnCpfF7sOdAx7U8tlZ4b7jf7H/qSFkqnmy12WByxcTSLUlAA4dlMQc0poBrJGapAp6yh8kZRCqVkOtP9vCNzjDxwHhRvYOoWqZS0v2/araOqrLVh57dymf2brmIxCncom2X7coZK2dRXrxMwGXmdU+Dg14AU1JRKfyEGYPQQBlSBTtnws7reDqtNwKDv+R90VvjzrrzUMPxkXgzGXsLrkFpNTDMBqEbRUTPMltbtDt51lEpPGxnK6qxuUveiqrDBbhfQ6fo/oFECqogwO2f2KaBwJEBBLcZRhGLM0FCEhvDmHuiiI3TQO+56VXW9m6VSZ6hY4c8rJEnCbbOikDsuzNdNIQ0ZlmTAwDg9WqxA0RF5e4sWq8Dbn8ql0m/k7JTbBsbqodMBlhaBCi+sI+0JZc/H+AG+Ld1O5G0cCVBQu2SQnAKRncJBXDDQ6STERnkn7a91DRUDcaK+IkkSJrYrn/7p3iZU1NgQH61nqXQPGPQSkuJ8W5jiy31NAIDUYRaf/H6ivsKAioLa3KmRWLs0Ebf/f1G+bgr1kzgvFaZo5AwVUb+Q0/6AgoPy7Mabn8izU9dPjeR6Ow/5stJfk9mO776Xg+K04QyoKLBwDRUFtRCDhLxUk6+bQf0ozrFurrLW3s0zu8Y1VET9IyclDDodUHLRiqIToTh4sgUhBmAOS6V7rHVz3/4PqPYcNKPFCiQn6DEwmil/FFg4EiCioKJU+uvtDBVT/oj6R2S4Dmkj5cITb3wmr5manhvBUuk94MsZKiXdb/L4MLAeBQUaBlREFFTivbS5L4tSEPUfZR1Vk0W+3m6azmIUPeGrvahsdoGviuWAakoGs0Io8HAkQERBJc5LRSmY8kfUf/LSWgsHpY0MZXn9HhrsoxmqgycsqK63I8IkYfxo9h0FHo4EiCioqEUpejtDZebGvkT9ZewloYiOlIcsN1wZ4ePW+C8l5a+qzo4mc+/WkXpCSfeblG7q1f5/RFrFgIqIgoqa8tfbNVSOlL8IpvwR9Tm9TsKKRbG4blI9rsphylhPRYbrEBUh37POV/TfLJUSUF3OdD8KUBwJEFFQiWuzhkoI0eP3YcofUf/KGReGaRlN0Ok4w9Ebg+P7N+3vXLkVJ8+3QKeTZ6iIAhFHAkQUVJSUP6sNqG3oecoLU/6IyB/1d2GKL4saAQCZo40YEM5hJwUmn5/Zzc3NWLFiBfLy8pCfn49Nmza5fN6CBQuQkpLS4d/y5cvV52zevBlTp05FdnY2VqxYgaampv76GETkJ0IMkpry0ptKf41M+SMiP5Sc2M8BlSPdb0omZ6cocPl8Y9+1a9eiuLgYW7Zswblz5/Dwww8jOTkZ1157rdPzXnjhBbS0tKg/FxYW4r777sP8+fMBAB988AFefPFFrFu3DvHx8Vi+fDnWrVuHxx9/vF8/DxFpX1yUHrUNdlTU2DAy2fPXCyGY8kdEfqk/K/3VN9lReKQZAMulU2Dz6UigsbERr7/+Oh599FGkp6dj5syZuOeee/Daa691eG5MTAwSExORmJiIuLg4PPvss7jnnnuQkZEBAHj11VexaNEiTJ8+HZmZmVi1ahW2bt3KWSoi6qC3hSnMzQJ2x/KrcBNT/ojIf6gpf2V9H1Dt2d8Emx24JMmAoQND+vz3EfmKTwOqQ4cOwWq1Ijs7Wz2Wm5uLwsJC2O2dr23Ytm0bampq8JOf/AQAYLPZsG/fPuTl5anPycrKQktLCw4dOtR3H4CI/FJclHzrq6jt2RoqZf2UTgcYQxhQEZH/UEqnl1ZaYbP3vDCPO9R0P85OUYDzacpfWVkZYmNjERrauslbQkICmpubUV1djbi4uA6vEULg5ZdfxsKFCxERIe9FUVtbi+bmZgwcOFB9nsFgQExMDC5cuOBRm2y23pVS9halHVppD3mOfahdsUpAVd3Sbf+46sfaBvmb3Ygwqcsvf0g7eD36P/ahd8RFAXod0GIFLlZaMDC2b4aCNpvAV/vlgGryeGOH/mM/+q9g6UNPPp9PA6qmpianYAqA+rPFYnH5mq+++goXLlzArbfeqh4zm81Or237Xp29T2f27dvn0fP7mtbaQ55jH2qPuc4EIBLHTlVg796Tbr2mbT+evmgAEAuDzoq9e/f2RROpj/B69H/sw96LjYxFea0Bn+4+jNGDW7p/QQ8cOx+CusYYhBvtsNQcRPtbJfvR/7EPW/k0oDIajR0CHuXnsLAwl6/54IMPcOWVVyImJsbpfdq+tu17mUyeTTNnZGRAr9d79Jq+oKQxaqU95Dn2oXZV2Rrx9ldVsOujkJU1usvnuupH2yEzgArERhmRlZXV9w2mXuP16P/Yh94z4otylNc2IyJmBLKyIvrkd3x1ogZAPS7PjEBOzjD1OPvR/wVLHyqf0x0+DaiSkpJQVVUFq9UKg0FuSllZGcLCwhAVFeXyNZ9++inuvfdep2MxMTEwGo0oLy/H6NHy4MhqtaK6uhqJiYketUmv12vq5NBae8hz7EPtSYyRF0dX1drd7pu2/Wi2yOumIsLZt/6G16P/Yx/2XnJCCIBmXKh0/x7oqd3FcvbQ5RMiXP4O9qP/Yx+28mlRitTUVBgMBqeUmYKCAmRkZECn69i0yspKlJSUIDc31+m4TqdDRkYGCgoK1GN79+6FwWDAuHHj+qz9ROSf4hxV/ip6WOWvQdmDipv6EpEf6uvNfU+XtuDMRSsMemBiquuMI6JA4tOAymQyYd68eVi5ciWKioqwY8cObNq0CQsXLgQgz1Yp66MA4MiRIzAajRg6dGiH95o/fz5eeeUV7NixA0VFRVi5ciVuvfVWj1P+iCjwKWXTm5oFmsyeF5VQqvyFcw8qIvJDyua+fbUX1ZdFcjGKCZeGcfNzCgo+39h3+fLlWLlyJRYtWoTIyEgsXboUs2bNAgDk5+dj9erVuOmmmwAAFRUViIqKgiR1/FZ4zpw5OHv2LB5//HFYLBbMmjULDz74YL9+FiLyD+FhOoQZJZibBSprbRjiYWDETX2JyJ8l9/HmviyXTsHG5wGVyWTCmjVrsGbNmg6PHT582Onn2bNnY/bs2Z2+1+LFi7F48WKvt5GIAk9clB7nyqyoqLVhiIcbTqopf9zUl4j80KB4efhXU29HQ5Pdq7NINfU2FB9rBgBcnsmAioIDv14loqAUHyWn/VX2YB0VU/6IyJ9FmHSIjpTvX96epfp6vxl2AYxKDlEDN6JAx9EAEQWl3hSmaGySU/4YUBGRv0ruo8IUTPejYMTRABEFpfgo+fZXUet5UYpGxwxVJFP+iMhP9UWlvxarwNcH5ICK6X4UTBhQEVFQUmaoqmo9n6Gqb2LKHxH5t8F9UJii6GgzGs0CsVE6pAwP9dr7EmkdRwNEFJTionqR8ueo8hfOcsBE5Kf6onT6l0WNAIDJ6SbodJzBp+DB0QARBSVlL6qeFKVQUv64sS8R+avkeO8GVEIIrp+ioMWAioiCkjpD1YOUv9ay6byFEpF/UlL+LlRYYbOJXr/fyfMtOF9hQ4gByE0N6/X7EfkTjgaIKCgpM1Q19Xa0WN0fTNjtAo3NrPJHRP4tIUaPEANgswNl1Z5/sdTel0Xy7FROShhMRt4bKbjwjCeioBQVoYPecQesrnN/MGG2CAhH/MWUPyLyVzqdpO4T5Y1Kf18w3Y+CGAMqIgpKOp3Uo8IUyqa+Bj0QGsKAioj8l1o6vax3AVVVnQ0HT1oAMKCi4MSAioiCVk/WUTW02dRXkhhQEZH/SvZS6fTdxU0QArh0WAgSYw3eaBqRX2FARURBK64Hlf5Y4Y+IAoW39qJS1k9xdoqCFQMqIgpaSmEKj1L+lE19WeGPiPycMkPVmzVUlhaBbw6aAQCXZ4Z7pV1E/oYjAiIKWnFR8i2wqtbu9msaHJv6RrDCHxH5OW9s7vvd92aYLQIJMXpcOizEW00j8iscERBR0FJnqDxYQ9WozFAx5Y+I/JxS5a+u0Y66Rve/WGpLTfcbb+K6UgpaDKiIKGj1psofN/UlIn9nMuoQ65ip78kslRACX7JcOhEDKiIKXj0rSsGUPyIKHIN7sRfV0TMtKKu2ISxUQnaK0dtNI/IbHBEQUdCKd8xQVdbaYLcLt17DohREFEh6s45KmZ3KHRcGYyjviRS8ePYTUdCKdQRUNjvcXj/AsulEFEjUSn9lLR6/luXSiWQMqIgoaIUYJERHyrdBd9dRKVX+wpnyR0QBoKd7UZVXW3H4tAWSBExmQEVBjiMCIgpqnhamUFL+WJSCiAJBcg8Dqt3F8t5T44aHqvdRomDFEQERBbW4Nuuo3MGUPyIKJMoMVWmVDVabe2tJAeCLokYATPcjAhhQEVGQU/eiYsofEQWhuCg9QkMk2O1AaaV7s1Rmix3fHm4GAFyeyYCKiCMCIgpqnpZOb2TKHxEFEJ1O8ngdVcEhMywtAklxeoxMDunL5hH5BY4IiCioxTs2taysda/KXwNT/ogowAyOl79YcjegaruZryTxXkjEgIqIgppalMKNNVR2u1A39uU+VEQUKFpLp3cfUNntwimgIiIGVEQU5DxJ+Wtqbl2wHcE1VEQUIJIT5bS98xXdB1SHT1tQVWtHeJiECZeG9XXTiPwCRwREFNTUohRuzFApJdNDDEBoCNNciCgwKGuozrmR8qfMTuWlhvE+SOTAgIqIglq8I+XP3CzUkuidUdZPscIfEQUStShFmRVCdF06/YsiOaC6nOl+RCqOCogoqJnCdDAZ5W9Zu9uLqpEl04koAClFKRrMArUNnX+xdKHCiuNnW6CTgMvGM6AiUnBUQERBTy1M0c06KnVTXxPTXIgocBhDdWr6c1eV/nYXy7NT6aOMiI7U90vbiPwBAyoiCnruFqZQ1lCxIAURBZpkN9ZRKel+rO5H5IyjAiIKevFulk5vYMofEQWo7jb3bTTbUXjEDIABFVF7HBUQUdBzd4aqkZv6ElGASk7sOqD65qAZLVZgSKIBlwwy9GfTiDSPARURBT1l7UBlbTdV/pSUP27qS0QBZnB815v7tt3MV5L4pRJRWxwVEFHQi4uSb4XdFaVgyh8RBSp1LyoXm/va7EItSMF0P6KOOCogoqCnVPnrtmx6E1P+iCgwKSl/ZVU2tFid96I6eMKCmno7Ik0SMsYYfdE8Ik1jQEVEQU9J+et+hoopf0QUmGIH6BAWKkEIoLTSeZbqC0e636R0Ewx6fqFE1B5HBUQU9JSAqrbB3uGb2bYampjyR0SBSZKk1rS/duuovixqBMB0P6LOcFRAREFvQLgOBscelVVdpP0pVf7CubEvEQWgZBel08+WteDUBSv0OnmGiog6YkBFREFPp5MQO6D7vahay6bz1klEgWewi819lep+GWOMGBDOex+RK7wyiIjg3l5USpU/rqEiokDkKqD6oojV/Yi6w1EBERHcK0yh7EMVzip/RBSA2m/uW99ox76jzQCAyxlQEXWKARUREYD4bkqn2+wCTc2OGSqm/BFRABrcZg2VEAJfH2iCzQ4MH2TAkIEhPm4dkXZxVEBEhDYpf7V2l4+bm1ur/7HKHxEFokFxBkgS0NQsUF1vZ7ofkZs4KiAiQvcpf0q6X4gBCA1hyh8RBZ7QEAkJjnthSWkLvt7PgIrIHQyoiIgAxEbJt8POilKoBSk4O0VEAUxZR/XhVw2obxKIitAhbZTRx60i0jaODIiI0LqGqrOy6a17UPG2SUSBS1lH9eHX8ma+k8eboNdxVp6oKz4fGTQ3N2PFihXIy8tDfn4+Nm3a1OlzDx8+jNtuuw2ZmZmYO3cudu/erT5WU1ODlJQUp3+XXXZZf3wEIgoASspfVa0Ndrvo8HijOkPFgQURBS4loLK0yPc8pvsRdc/g6wasXbsWxcXF2LJlC86dO4eHH34YycnJuPbaa52eV1dXh7vuugszZszA008/jbfeegv33nsvPvjgA8THx+Po0aOIiYnBv/71L/U1Op3P40Ui8hOxUXpIEmCzAzUNdnWjX4Wyhoopf0QUyJITWoeGIQZgYlqYD1tD5B98GlA1Njbi9ddfx5/+9Cekp6cjPT0dR44cwWuvvdYhoNq+fTvCw8OxcuVK6PV6LFu2DJ988gmKi4sxbdo0HD9+HCNHjkRiYqKPPg0R+TODXkJ0hA7V9XZU1tg6BFTKDBVT/ogokLUNqCZcGsaqpkRu8OlVcujQIVitVmRnZ6vHcnNzUVhYCLvduXTx119/jR/84AfQ61sHOVu3bsW0adMAAEePHsWIESP6pd1EFJjiutiLqsGszFAx5Y+IAtfgNgEV0/2I3OPTGaqysjLExsYiNDRUPZaQkIDm5mZUV1cjLi5OPV5SUoLMzEw89thj+OijjzBkyBA8/PDDyM3NBQAcO3YMVqsVt9xyC0pLS5GXl4fly5dj4MCBHrXJZnO9IL2/Ke3QSnvIc+xD/xMbpQPOAWVVLbDZ5PuS0n/1jfL/mowS+9QP8Xr0f+zD/hFpEhgUp0dlnQ2T00O9/t+b/ej/gqUPPfl8Pg2ompqanIIpAOrPFovF6XhjYyM2btyIhQsX4k9/+hPeffdd3H333Xj//fcxePBgHD9+HHFxcVi+fDmEEHj22Wfxs5/9DK+//rrTrFZ39u3b1/sP5kVaaw95jn3oR2wDAIRh/+EzSAprcnro9NkyAOGory3D3r0nfdE68gJej/6Pfdj37rxaB4tVwrnTZTh3um9+B/vR/7EPW/k0oDIajR0CJ+XnsDDnRZB6vR6pqalYtmwZACAtLQ2ff/453nrrLfzsZz/Du+++C0mS1Nc9//zzyM/PR2FhIXJyctxuU0ZGhkcBWF+x2WzYt2+fZtpDnmMf+p+C0zUoOFIPY0QSsrJiALT2Y3hEPIAmjLxkELKyBvi0neQ5Xo/+j30YGNiP/i9Y+lD5nO7waUCVlJSEqqoqWK1WGAxyU8rKyhAWFoaoqCin5yYmJmLUqFFOx0aMGIHz588DAEwm5zzf+Ph4xMTEoLS01KM26fV6TZ0cWmsPeY596D8SYkIAAFV1okOfNVnkohSREexPf8br0f+xDwMD+9H/sQ9b+bQoRWpqKgwGA/bu3aseKygoQEZGRoeS51lZWTh8+LDTsePHj2PIkCGor6/HxIkTnfalKi0tRVVVVYcgjIioM3FR8n2nssZFUYomZR8qVrwiIiKiVj4dGZhMJsybNw8rV65EUVERduzYgU2bNmHhwoUA5Nkqs9kMAPjRj36Ew4cP44UXXsCpU6ewfv16lJSU4IYbbkBkZCRyc3OxevVqFBUVYf/+/bj//vsxdepUpKSk+PIjEpEfiXNs7lvhospfo6PKXzir/BEREVEbPv+qdfny5UhPT8eiRYuwatUqLF26FLNmzQIA5Ofn47333gMADBkyBC+//DI+/vhjXHfddfj444+xceNGJCUlAQDWrFmDtLQ0LF68GAsWLMCQIUPwzDPP+OxzEZH/iVfKptfYIIRweqzBsQ9VBPehIiIiojZ8uoYKkGep1qxZgzVr1nR4rH2KX25uLrZt2+byfaKjo7F69eo+aSMRBQdlhspsEWg0C0SYWmejGpqUfagYUBEREVErjgyIiBxMRp2a0td+c99GxwxVOGeoiIiIqA2ODIiI2ohrk/ansNvlWSsAiOAaKiIiImqDARURURtKQNW2MEVzS2sQFc6UPyIiImqDIwMiojbilUp/bWaozBY5oAoNkRBi4AwVERERtWJARUTUhlKYom3Kn9kxQ8V0PyIiImqPARURURtq6fRaFwEVC1IQERFROxwdEBG1ERcl3xYra+3qMbNFPsb1U0RERNQeRwdERG3EuVhD1exYQxXOlD8iIiJqhwEVEVEbSlEKlyl/nKEiIiKidjg6ICJqQymbXttgh6VF3nuKa6iIiIioMxwdEBG1ERWhg0GOqdRZKjNT/oiIiKgTDKiIiNqQJEmdpapyBFTKGiqm/BEREVF7HB0QEbXTvjCFuUW+VTLlj4iIiNrj6ICIqB1lL6oKpvwRERFRNxhQERG1o8xQVaozVCxKQURERK5xdEBE1I5SOr2i3RoqbuxLRERE7XF0QETUjlKUouMMFVP+iIiIyBkDKiKiduKi5FtjZa0dQOsaKlb5IyIiovY4OiAiaie+kyp/TPkjIiKi9jg6ICJqRylKUVVnQ4tVoMXKlD8iIiJyjQEVEVE7cQP0kCTAbgfOl1vV45yhIiIiovY4OiAiakevlxATKd8ez1yUAypjiASDnjNURERE5IwBFRGRC7GOSn8ljoCKm/oSERGRKwyoiIhcUApTnCmVAypu6ktERESucIRARORCHGeoiIiIyA0MqIiIXFBmqEpKlYCKt0siIiLqiCMEIiIXlBmq2gZ5c98IzlARERGRCwyoiIhcUPaiUnCGioiIiFzhCIGIyIX4KOfbIzf1JSIiIlcYUBERucAZKiIiInIHRwhERC7EtwuouIaKiIiIXGFARUTkQliozimI4gwVERERucIRAhFRJ2KjWmepuA8VERERucKAioioE23T/iJMvF0SERFRRxwhEBF1om1hCq6hIiIiIlcYUBERdSLeKeWPt0siIiLqiCMEIqJOtJ2h4hoqIiIicoUBFRFRJ+KiuIaKiIiIusYRAhFRJ9oWpQg3coaKiIiIOmJARUTUiQRHQGUMsUOvZ0BFREREHRl83QAiIq26ZJAB86ZFQDSX+ropREREpFGcoSIi6oQkSVhySwzy05t83RQiIiLSKAZUREREREREPcSAioiIiIiIqIcYUBEREREREfUQAyoiIiIiIqIeYkBFRERERETUQwyoiIiIiIiIeogBFRERERERUQ/5PKBqbm7GihUrkJeXh/z8fGzatKnT5x4+fBi33XYbMjMzMXfuXOzevdvp8c2bN2Pq1KnIzs7GihUr0NTEvWOIiIiIiKjv+DygWrt2LYqLi7Flyxb85je/wYsvvoh///vfHZ5XV1eHu+66C2PGjME777yDmTNn4t5770VFRQUA4IMPPsCLL76I3/72t9iyZQsKCwuxbt26/v44REREREQURHwaUDU2NuL111/Ho48+ivT0dMycORP33HMPXnvttQ7P3b59O8LDw7Fy5UoMHz4cy5Ytw/Dhw1FcXAwAePXVV7Fo0SJMnz4dmZmZWLVqFbZu3cpZKiIiIiIi6jM+DagOHToEq9WK7Oxs9Vhubi4KCwtht9udnvv111/jBz/4AfR6vXps69atmDZtGmw2G/bt24e8vDz1saysLLS0tODQoUN9/0GIiIiIiCgoGXz5y8vKyhAbG4vQ0FD1WEJCApqbm1FdXY24uDj1eElJCTIzM/HYY4/ho48+wpAhQ/Dwww8jNzcXtbW1aG5uxsCBA9XnGwwGxMTE4MKFCx61yWaz9f6DeYHSDq20hzzHPgwM7MfAwH70f+zDwMB+9H/B0oeefD6fBlRNTU1OwRQA9WeLxeJ0vLGxERs3bsTChQvxpz/9Ce+++y7uvvtuvP/++x1e2/bn9u/TnX379nn0/L6mtfaQ59iHgYH9GBjYj/6PfRgY2I/+j33YyqcBldFo7BDwKD+HhYU5Hdfr9UhNTcWyZcsAAGlpafj888/x1ltv4dZbb3V6bdv3MplMHrUpIyPDKa3QV5Q0Rq20hzzHPgwM7MfAwH70f+zDwMB+9H/B0ofK53SHTwOqpKQkVFVVwWq1wmCQm1JWVoawsDBERUU5PTcxMRGjRo1yOjZixAicP38eMTExMBqNKC8vx+jRowEAVqsV1dXVSExM9KhNer1eUyeH1tpDnmMfBgb2Y2BgP/o/9mFgYD/6P/ZhK58WpUhNTYXBYMDevXvVYwUFBcjIyIBO59y0rKwsHD582OnY8ePHMWTIEOh0OmRkZKCgoEB9bO/evTAYDBg3blyffgYiIiIiIgpePg2oTCYT5s2bh5UrV6KoqAg7duzApk2bsHDhQgDybJXZbAYA/OhHP8Lhw4fxwgsv4NSpU1i/fj1KSkpwww03AADmz5+PV155BTt27EBRURFWrlyJW2+91eOUPyIiIiIiInf5fGPf5cuXIz09HYsWLcKqVauwdOlSzJo1CwCQn5+P9957DwAwZMgQvPzyy/j4449x3XXX4eOPP8bGjRuRlJQEAJgzZw5++tOf4vHHH8ddd92FzMxMPPjggz77XEREREREFPh8uoYKkGep1qxZgzVr1nR4rH2KX25uLrZt29bpey1evBiLFy/uUTuEEAC0UwIyWEpSBjL2YWBgPwYG9qP/Yx8GBvaj/wuWPlQ+nxIjdEUS7jwrCFgsFpZ/JCIiIiIiVUZGRoetmdpjQOVgt9thtVqh0+kgSZKvm0NERERERD4ihIDdbofBYOhQLK89BlREREREREQ95POiFERERERERP6KARUREREREVEPMaAiIiIiIiLqIQZUREREREREPcSAioiIiIiIqIcYUBEREREREfUQAyoiIiIiIqIeYkClMc3NzVixYgXy8vKQn5+PTZs2+bpJ5MKHH36IlJQUp3/Lli0DABw4cAA//OEPMWHCBNx8880oLi52eu2//vUvXH311ZgwYQKWLFmCyspKX3yEoGaxWHDdddfhq6++Uo+VlJTgjjvuQFZWFmbPno3PPvvM6TVffPEFrrvuOkyYMAELFy5ESUmJ0+ObN2/G1KlTkZ2djRUrVqCpqalfPkswc9WPTz75ZIdr869//av6eFfXnxACzzzzDCZPnoxJkyZh7dq1sNvt/fqZgkVpaSmWLVuGSZMmYerUqVi9ejWam5sB8Fr0J131I69F/3Hq1CncfffdyM7OxlVXXYWXX35ZfYzXo5sEacpvf/tbMXfuXFFcXCz+85//iOzsbPH+++/7ulnUzv/+7/+Kn/70p+LixYvqv5qaGtHQ0CCuuOIK8fTTT4ujR4+KJ554Qlx++eWioaFBCCFEYWGhyMzMFNu3bxcHDx4Ut99+u1i8eLGPP01wMZvNYsmSJWLs2LFi9+7dQggh7Ha7mDt3rnjggQfE0aNHxUsvvSQmTJggzp49K4QQ4uzZsyIrK0u88sor4vvvvxe//OUvxXXXXSfsdrsQQoh///vfIjc3V3z00UeisLBQzJ49W6xatcpnnzEYuOpHIYS44447xIYNG5yuzcbGRiFE99ffK6+8IqZNmyb27NkjvvzyS5Gfny9efvnlfv9sgc5ut4tbb71V3HPPPeL7778Xe/bsETNnzhRPP/00r0U/0lU/CsFr0V/YbDYxa9Ys8cADD4gTJ06InTt3ipycHPH222/zevQAAyoNaWhoEBkZGU6Dgz/84Q/i9ttv92GryJUHHnhA/P73v+9w/PXXXxczZsxQbyZ2u13MnDlTbN26VQghxIMPPigefvhh9fnnzp0TKSkp4vTp0/3T8CB35MgRcf3114u5c+c6DcS/+OILkZWVpQa+QgixaNEi8fzzzwshhHjuueecrsPGxkaRnZ2tvn7+/Pnqc4UQYs+ePSIzM1MdPJB3ddaPQggxdepU8emnn7p8XXfX37Rp09RrVQgh3nzzTTF9+vQ++hTB6+jRo2Ls2LGirKxMPfbOO++I/Px8Xot+pKt+FILXor8oLS0Vv/zlL0VdXZ16bMmSJeI3v/kNr0cPMOVPQw4dOgSr1Yrs7Gz1WG5uLgoLCznVrTHHjh3DiBEjOhwvLCxEbm4uJEkCAEiShJycHOzdu1d9PC8vT33+4MGDkZycjMLCwv5odtD7+uuvcdlll+Ef//iH0/HCwkKkpaUhPDxcPZabm9tpv5lMJqSnp2Pv3r2w2WzYt2+f0+NZWVloaWnBoUOH+vYDBanO+rG+vh6lpaUur02g6+uvtLQU58+fx8SJE9XHc3NzcfbsWVy8eLFPPkewSkxMxMsvv4yEhASn4/X19bwW/UhX/chr0X8MHDgQzz33HCIjIyGEQEFBAfbs2YNJkybxevSAwdcNoFZlZWWIjY1FaGioeiwhIQHNzc2orq5GXFycD1tHCiEETpw4gc8++wwbNmyAzWbDtddei2XLlqGsrAxjxoxxen58fDyOHDkCALh48SIGDhzY4fELFy70W/uD2fz5810eLysr67Jfunq8trYWzc3NTo8bDAbExMSwX/tIZ/147NgxSJKEl156Cbt27UJMTAzuvPNO3HjjjQC6vv7KysoAwOlxZaB44cKFDq+jnouKisLUqVPVn+12O/76179i8uTJvBb9SFf9yGvRP82YMQPnzp3D9OnTcc011+Cpp57i9egmBlQa0tTU5BRMAVB/tlgsvmgSuXDu3Dm1r5577jmcOXMGTz75JMxmc6d9qPSf2Wzu8nHyje76ravHzWaz+nNnr6f+cfz4cUiShFGjRuH222/Hnj178NhjjyEyMhIzZ87s8vpz1Y+8//aPdevW4cCBA3jjjTewefNmXot+qm0/7t+/n9eiH3r++edRXl6OlStXYvXq1fzb6AEGVBpiNBo7nGTKz2FhYb5oErkwZMgQfPXVV4iOjoYkSUhNTYXdbseDDz6ISZMmuexDpf8662OTydRv7aeOjEYjqqurnY65029RUVEwGo3qz+0fZ7/2r3nz5mH69OmIiYkBAIwbNw4nT57E3/72N8ycObPL66/tgK19n7If+866deuwZcsWPPvssxg7diyvRT/Vvh8vvfRSXot+KCMjA4Bccfq///u/cfPNN3eoysfr0TWuodKQpKQkVFVVwWq1qsfKysoQFhaGqKgoH7aM2ouJiVHXSQHA6NGj0dzcjMTERJSXlzs9t7y8XJ3yTkpKcvl4YmJi3zeaOtVZv7jTbzExMTAajU6PW61WVFdXs1/7mSRJ6gBOMWrUKJSWlgLouh+TkpIAQE03avv/2Y9944knnsCf//xnrFu3Dtdccw0AXov+yFU/8lr0H+Xl5dixY4fTsTFjxqClpaVXY5pgux4ZUGlIamoqDAaDutgPAAoKCpCRkQGdjl2lFZ9++ikuu+wyp29tDh48iJiYGOTm5uK7776DEAKAvN7q22+/xYQJEwAAEyZMQEFBgfq68+fP4/z58+rj5BsTJkzA/v371RQFQL72Ouu3pqYmHDhwABMmTIBOp0NGRobT43v37oXBYMC4ceP670MQ1q9fjzvuuMPp2KFDhzBq1CgAXV9/SUlJSE5Odnq8oKAAycnJXLPRB1588UX8/e9/x//8z/9gzpw56nFei/6ls37kteg/zpw5g3vvvVcNdgGguLgYcXFxyM3N5fXoLt8WGaT2HnvsMTFnzhxRWFgoPvzwQ5GTkyM++OADXzeL2qirqxNTp04Vv/rVr8SxY8fEzp07RX5+vti4caOoq6sTkydPFk888YQ4cuSIeOKJJ8QVV1yhlhz99ttvRXp6uvjnP/+p7r3x05/+1MefKDi1LbdttVrF7NmzxX333Se+//57sWHDBpGVlaXutVFSUiIyMjLEhg0b1L025s6dq5bH/9e//iVycnLEhx9+KAoLC8WcOXPEE0884bPPFkza9mNhYaFIS0sTL7/8sjh16pR47bXXxPjx48W3334rhOj++tuwYYPIz88Xu3fvFrt37xb5+fli06ZNPvlcgezo0aMiNTVVPPvss057FF28eJHXoh/pqh95LfoPq9UqbrrpJnHXXXeJI0eOiJ07d4rLL79cbN68mdejBxhQaUxjY6N46KGHRFZWlsjPzxd//vOffd0kcuH7778Xd9xxh8jKyhJXXHGFeOGFF9QbSGFhoZg3b57IyMgQt9xyi9i/f7/Ta7du3SqmTZsmsrKyxJIlS0RlZaUvPkLQa79/0cmTJ8WPf/xjMX78eDFnzhzx+eefOz1/586dYtasWSIzM1MsWrSow95hGzZsEFOmTBG5ubli+fLlwmw298vnCHbt+/HDDz8Uc+fOFRkZGeLaa6/t8IVUV9ef1WoVTz31lMjLyxOXXXaZWLdunXpdk/ds2LBBjB071uU/IXgt+ovu+pHXov+4cOGCWLJkicjJyRFXXHGF+OMf/6j+9+b16B5JCEduEhEREREREXmEC3OIiIiIiIh6iAEVERERERFRDzGgIiIiIiIi6iEGVERERERERD3EgIqIiIiIiKiHGFARERERERH1EAMqIiIiIiKiHmJARURERERE1EMGXzeAiIgC2yOPPILt27d3+nhCQgI+//zzfmwRkJKSgnvvvRdLly7t9rlPPvkkzGYznnzySSxYsAAA8Je//KXXbZgxYwYmTZqEp59+utfvRUREvsOAioiI+lxiYiJefPFFl4+FhIT0c2s8s2vXLjz44IO+bgYREWkUAyoiIupzoaGhyMrK8nUzPHbq1CmcO3cOU6ZM8XVTiIhIo7iGioiINGHBggV45JFH8NJLL+Hyyy9Hbm4ufvGLX+Ds2bNOz9u3bx/uvvtuXHbZZcjJycHPfvYzHDlyxOk5Fy9exMMPP4wpU6YgOzsbt99+O7777jun59TX1+PRRx/FpEmTkJ2djWXLlqG8vNzpOZ988glycnIQGRnpss2PPPII7rjjDmzduhXXXHMNxo8fjxtuuAG7du1yet6hQ4dw5513Ijs7G9OnT8fbb7/d4b3sdjs2btyImTNnYvz48bjmmmucUguLi4uRnp6ORx55RD1WUVGBKVOm4M4774QQoov/ukRE1FcYUBERUb+wWq0u/7UNBP7v//4P27Ztw69//WusWrUKBw8exIIFC9DU1AQA2L17N2677TYAwFNPPYUnn3wS58+fx49+9CMcO3YMANDQ0IDbbrsNX331FR588EG8+OKLMBqNuOuuu3Dy5En1d7366qtoaWnB+vXr8cADD+Cjjz7Cb3/7W6c2f/LJJ5g2bVqXn6u4uBivvPIKli1bhj/84Q/Q6/VYunQpampqAAClpaW4/fbbUVdXh3Xr1uGXv/wlnnnmGZSWljq9z8qVK/H888/j+uuvx0svvYRrr70WTz31FP7whz8AAMaPH4+f/OQn2L59O7788ksAwOOPPw673Y6nn34akiR52iVEROQFTPkjIqI+d/bsWaSnp7t87KGHHsLdd98NAGhqasK2bdswbNgwAMCoUaNw44034s0338Rtt92G3//+9xg+fDg2btwIvV4PAMjPz8fMmTPx/PPPY/369di+fTvOnj2L7du3IzU1FQCQk5ODefPmYc+ePRgxYgQAICMjA2vXrgUATJkyBYWFhfjkk0/UdpnNZuzZswfLly/v8rPV1dVh27ZtuOSSSwAA4eHhuP3227F7925cc8012Lx5M2w2GzZu3Ii4uDgAwMiRI3Hrrbeq73HixAn885//xK9+9SssXrxY/VySJGHDhg2YP38+YmNjsWTJEnz00UdYtWoVFi9ejB07dmD9+vVISkpyvzOIiMirGFAREVGfS0xMxB//+EeXjw0ePFj9/zk5OWowBQBpaWkYNmwY9uzZgxtuuAH79u3DvffeqwZTABAVFYXp06erwVBBQQGGDh2qBlMAYDKZ8MEHHzj93tzcXKefhw4ditraWvXn3bt3IyEhAWPGjOnys8XFxanBFAAMGjQIANRZtYKCAmRlZanBFABMmDABycnJTr9LCIEZM2bAarWqx2fMmIE//vGPKCgowNVXX42QkBCsWbMGP/zhD/Hoo4/ixhtvxLXXXttl+4iIqG8xoCIioj4XGhqKjIyMbp/naqYlPj4eNTU1qKurgxACCQkJHZ6TkJCAuro6AEB1dTXi4+O7/V3h4eFOP+t0Oqf0w08++QRTp07t9n1MJpPTz0rqnd1uBwDU1NRg6NChHV6XmJio/v/q6moAwJw5c1z+jrbpgampqUhJSUFxcTGmT5/ebfuIiKhvMaAiIiLNqKqq6nCsvLwcl1xyCQYMGABJkjoUjgCAsrIyxMTEAAAGDBiAM2fOdHjOt99+i+joaIwePdqttuzatQuPPvqoZx/AhdjYWJdtVoIoQJ5lA4AtW7YgIiKiw3Pbzmb94x//QHFxMcaNG4ff/e53mDJlivp6IiLqfyxKQUREmlFQUOAUVBUXF+PMmTOYMmUKwsPDMX78eLz//vuw2Wzqc+rq6rBz5041hS8vLw8lJSVOlf+am5uxdOlSvPHGG26149ixY7h48SImT57c6880efJkfPfdd06zTEePHkVJSYn6c15eHgA5oMzIyFD/VVZWYv369WrwdfbsWaxZswa33HILXnrpJdTV1eF3v/tdr9tIREQ9xxkqIiLqcxaLBXv37u308ZSUFADyuqN77rkHP//5z9HQ0IBnn30WY8eOxXXXXQcAeOCBB3D33Xdj8eLFmD9/PlpaWrBx40ZYLBYsWbIEAHDTTTfhL3/5C37+859j2bJliI2NVSv6zZ8/36327tq1CxMnTuyQFtgTixYtwhtvvIG7774bS5cuhc1mw7PPPuu0oXFKSgquv/56PPbYYzh79izGjx+PEydO4Nlnn8XQoUMxYsQICCHw6KOPwmQy4aGHHkJ0dDTuu+8+PPXUU7jmmmswY8aMXreViIg8x4CKiIj6XFlZGf7rv/6r08fffPNNAPJMzeTJk9VUuxkzZuChhx5CaGgoALka35///Gc8//zz+NWvfoXQ0FDk5eVhzZo1uPTSSwEAkZGR+Otf/4q1a9fiiSeegN1uR1ZWFl599VWnghdd2bVrV7fl0t0VGxuLv/3tb/jd736HRx55BBEREbjnnnvw3nvvOT1v9erV2LBhA/7+97/jwoULiI+Px+zZs3HfffdBr9fjtddew5dffonnnnsO0dHRAOS9u9555x08/vjjyMnJUdMeiYio/0iCOwESEZEGLFiwAACcNrMlIiLSOq6hIiIiIiIi6iEGVERERERERD3ElD8iIiIiIqIe4gwVERERERFRDzGgIiIiIiIi6iEGVERERERERD3EgIqIiIiIiKiHGFARERERERH1EAMqIiIiIiKiHmJARURERERE1EMMqIiIiIiIiHro/wHzTk64DaApiwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_loss' values from the 'results_df' DataFrame.\n",
    "# We drop any NaN values to ensure a smooth plot.\n",
    "plt.figure(figsize=(10, 6))  # Setting a suitable figure size for better visibility\n",
    "results_df['valid_loss'].dropna().plot(color=\"royalblue\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Loss Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Validation Loss', fontsize=12)\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_mean_token_accuracy' values from the 'results_df' DataFrame.\n",
    "# We omit any NaN values to ensure a smooth, continuous plot.\n",
    "plt.figure(figsize=(10, 6))  # Designating an optimal figure size for better clarity\n",
    "results_df['valid_mean_token_accuracy'].dropna().plot(color=\"darkgreen\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Mean Token Accuracy Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Mean Token Accuracy', fontsize=12)\n",
    "\n",
    "# Showcasing the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basically no change in performance.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's see what happens if the data was sorted by star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in the legacy API, you had to shuffle the data yourself before uploading it or else you were \n",
    "#  subject to severely worse performance from the fine-tuning\n",
    "\n",
    "# Let's see if this is still true.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upload a new file that is specifically sorted by star\n",
    "ordered_training_file = client.files.create(\n",
    "  file=open(\"app-review-full-train-sentiment-ordered.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = client.fine_tuning.jobs.create(\n",
    "    training_file=ordered_training_file.id,\n",
    "    validation_file=val_file.id,\n",
    "    model='babbage-002',\n",
    "    hyperparameters={'n_epochs': 1}\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ftjob-qdXc2PVcEl3ENr9b1YemfGEr'"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"object\": \"fine_tuning.job\",\n",
      "  \"id\": \"ftjob-qdXc2PVcEl3ENr9b1YemfGEr\",\n",
      "  \"model\": \"babbage-002\",\n",
      "  \"created_at\": 1698422344,\n",
      "  \"finished_at\": 1698428920,\n",
      "  \"fine_tuned_model\": \"ft:babbage-002:personal::8ELEnbVw\",\n",
      "  \"organization_id\": \"org-azQm6jrJ3FSBpJAwYcgxQxGu\",\n",
      "  \"result_files\": [\n",
      "    \"file-mUr8uYRZGeAjt5CTrHpEwEta\"\n",
      "  ],\n",
      "  \"status\": \"succeeded\",\n",
      "  \"validation_file\": \"file-yF5vsOt3IMOhJs2jFeK46nWo\",\n",
      "  \"training_file\": \"file-JRecjWOWPenIwB6A5zEhmWz2\",\n",
      "  \"hyperparameters\": {\n",
      "    \"n_epochs\": 1\n",
      "  },\n",
      "  \"trained_tokens\": 2833557,\n",
      "  \"error\": null\n",
      "}\n",
      "['file-mUr8uYRZGeAjt5CTrHpEwEta']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_mean_token_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.20046</td>\n",
       "      <td>0.51111</td>\n",
       "      <td>2.09622</td>\n",
       "      <td>0.47778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.57506</td>\n",
       "      <td>0.25556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.10734</td>\n",
       "      <td>0.47778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.60895</td>\n",
       "      <td>0.31111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.28541</td>\n",
       "      <td>0.41111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  train_loss  train_accuracy  valid_loss  valid_mean_token_accuracy\n",
       "0     1     2.20046         0.51111     2.09622                    0.47778\n",
       "1     2     2.57506         0.25556         NaN                        NaN\n",
       "2     3     2.10734         0.47778         NaN                        NaN\n",
       "3     4     2.60895         0.31111         NaN                        NaN\n",
       "4     5     2.28541         0.41111         NaN                        NaN"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = client.fine_tuning.jobs.retrieve(job.id)\n",
    "print(job)\n",
    "results_df = None\n",
    "if len(job.result_files):\n",
    "    print(job.result_files)\n",
    "    results = client.files.retrieve_content(job.result_files[0])\n",
    "    with open('results.csv', 'w') as f:\n",
    "        f.write(results)\n",
    "    results_df = pd.read_csv('results.csv')\n",
    "\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_loss' values from the 'results_df' DataFrame.\n",
    "# We drop any NaN values to ensure a smooth plot.\n",
    "plt.figure(figsize=(10, 6))  # Setting a suitable figure size for better visibility\n",
    "results_df['valid_loss'].dropna().plot(color=\"royalblue\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Loss Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Validation Loss', fontsize=12)\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_mean_token_accuracy' values from the 'results_df' DataFrame.\n",
    "# We omit any NaN values to ensure a smooth, continuous plot.\n",
    "plt.figure(figsize=(10, 6))  # Designating an optimal figure size for better clarity\n",
    "results_df['valid_mean_token_accuracy'].dropna().plot(color=\"darkgreen\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Mean Token Accuracy Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Mean Token Accuracy', fontsize=12)\n",
    "\n",
    "# Showcasing the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looks like about the same result. The legacy API showed a huge difference in performance but the new version\n",
    "#  seems to be shuffling the data in the backend even though I can't find reference to this behavior in their documentation.\n",
    "#  We love that though!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 training - first with no system prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [{'role': 'system', 'content': ''},\n",
       "  {'role': 'user', 'content': 'Nice😉'},\n",
       "  {'role': 'assistant', 'content': '4'}]}"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reminder of what this data looks like\n",
    "json.loads(open(\"app-review-full-train-sentiment-random-3.5.jsonl\", \"rb\").readlines()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upload our GPT 3.5 training data\n",
    "training_file_3_5 = client.files.create(\n",
    "  file=open(\"app-review-full-train-sentiment-random-3.5.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")\n",
    "\n",
    "# Creating a file object for the validation dataset with OpenAI's API.\n",
    "val_file_3_5 = client.files.create(\n",
    "  file=open(\"app-review-full-val-sentiment-random-3.5.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = client.fine_tuning.jobs.create(\n",
    "    training_file=training_file_3_5.id,\n",
    "    validation_file=val_file_3_5.id,\n",
    "    model='gpt-3.5-turbo',\n",
    "    hyperparameters={'n_epochs': 1}\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ftjob-k1yjidnRUBB8OtldUTmTtPXm'"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"object\": \"fine_tuning.job\",\n",
      "  \"id\": \"ftjob-k1yjidnRUBB8OtldUTmTtPXm\",\n",
      "  \"model\": \"gpt-3.5-turbo-0613\",\n",
      "  \"created_at\": 1699021269,\n",
      "  \"finished_at\": 1699032323,\n",
      "  \"fine_tuned_model\": \"ft:gpt-3.5-turbo-0613:personal::8GsD6MhX\",\n",
      "  \"organization_id\": \"org-azQm6jrJ3FSBpJAwYcgxQxGu\",\n",
      "  \"result_files\": [\n",
      "    \"file-mTlEML9tGO9bo2uErXfSFbfd\"\n",
      "  ],\n",
      "  \"status\": \"succeeded\",\n",
      "  \"validation_file\": \"file-Uy4kKuUgtEokG3FShPuqyGNG\",\n",
      "  \"training_file\": \"file-lezxzS3TNNroTXHBwfF2Z7ga\",\n",
      "  \"hyperparameters\": {\n",
      "    \"n_epochs\": 1\n",
      "  },\n",
      "  \"trained_tokens\": 4985412,\n",
      "  \"error\": null\n",
      "}\n",
      "['file-mTlEML9tGO9bo2uErXfSFbfd']\n"
     ]
    }
   ],
   "source": [
    "job = client.fine_tuning.jobs.retrieve(job.id)\n",
    "print(job)\n",
    "results_df = None\n",
    "if len(job.result_files):\n",
    "    print(job.result_files)\n",
    "    results = client.files.retrieve_content(job.result_files[0])\n",
    "    with open('results.csv', 'w') as f:\n",
    "        f.write(results)\n",
    "    results_df = pd.read_csv('results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_mean_token_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.24463</td>\n",
       "      <td>0.36232</td>\n",
       "      <td>4.60071</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.87384</td>\n",
       "      <td>0.35942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.43191</td>\n",
       "      <td>0.36232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.11990</td>\n",
       "      <td>0.36522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.16018</td>\n",
       "      <td>0.35072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  train_loss  train_accuracy  valid_loss  valid_mean_token_accuracy\n",
       "0     1     4.24463         0.36232     4.60071                        0.0\n",
       "1     2     3.87384         0.35942         NaN                        NaN\n",
       "2     3     4.43191         0.36232         NaN                        NaN\n",
       "3     4     4.11990         0.36522         NaN                        NaN\n",
       "4     5     4.16018         0.35072         NaN                        NaN"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_loss' values from the 'results_df' DataFrame.\n",
    "# We drop any NaN values to ensure a smooth plot.\n",
    "plt.figure(figsize=(10, 6))  # Setting a suitable figure size for better visibility\n",
    "results_df['valid_loss'].dropna().plot(color=\"royalblue\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Loss Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Validation Loss', fontsize=12)\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n",
    "\n",
    "# Plotting the 'valid_mean_token_accuracy' values from the 'results_df' DataFrame.\n",
    "# We omit any NaN values to ensure a smooth, continuous plot.\n",
    "plt.figure(figsize=(10, 6))  # Designating an optimal figure size for better clarity\n",
    "results_df['valid_mean_token_accuracy'].dropna().plot(color=\"darkgreen\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Mean Token Accuracy Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Mean Token Accuracy', fontsize=12)\n",
    "\n",
    "# Showcasing the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 training with a system prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [{'role': 'system',\n",
       "   'content': 'You predict star ratings given an app review from 0-4 where 0 means the review would give the worst rating and 4 means the best rating'},\n",
       "  {'role': 'user', 'content': 'Nice😉'},\n",
       "  {'role': 'assistant', 'content': '4'}]}"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reminder of what this data looks like\n",
    "json.loads(open(\"app-review-full-train-sentiment-random-3.5-system.jsonl\", \"rb\").readlines()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_file_3_5_system = client.files.create(\n",
    "  file=open(\"app-review-full-train-sentiment-random-3.5-system.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")\n",
    "\n",
    "# Creating a file object for the validation dataset with OpenAI's API.\n",
    "val_file_3_5_system = client.files.create(\n",
    "  file=open(\"app-review-full-val-sentiment-random-3.5-system.jsonl\", \"rb\"),\n",
    "  purpose='fine-tune'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = client.fine_tuning.jobs.create(\n",
    "    training_file=training_file_3_5_system.id,\n",
    "    validation_file=val_file_3_5_system.id,\n",
    "    model='gpt-3.5-turbo',\n",
    "    hyperparameters={'n_epochs': 1}\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ftjob-ED2IHyq6vsHzDTQUQeSaHxbo'"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"object\": \"fine_tuning.job\",\n",
      "  \"id\": \"ftjob-ED2IHyq6vsHzDTQUQeSaHxbo\",\n",
      "  \"model\": \"gpt-3.5-turbo-0613\",\n",
      "  \"created_at\": 1699134693,\n",
      "  \"finished_at\": 1699146913,\n",
      "  \"fine_tuned_model\": \"ft:gpt-3.5-turbo-0613:personal::8HM1JKgr\",\n",
      "  \"organization_id\": \"org-azQm6jrJ3FSBpJAwYcgxQxGu\",\n",
      "  \"result_files\": [\n",
      "    \"file-sOKK41WCQrgWL1AeDdErYqh7\"\n",
      "  ],\n",
      "  \"status\": \"succeeded\",\n",
      "  \"validation_file\": \"file-kE0BTJcFt3FOI8AQKPhmrJHk\",\n",
      "  \"training_file\": \"file-5S2BXVp2tdqd2qxh955c4uYT\",\n",
      "  \"hyperparameters\": {\n",
      "    \"n_epochs\": 1\n",
      "  },\n",
      "  \"trained_tokens\": 10343421,\n",
      "  \"error\": null\n",
      "}\n",
      "['file-sOKK41WCQrgWL1AeDdErYqh7']\n"
     ]
    }
   ],
   "source": [
    "job = client.fine_tuning.jobs.retrieve(job.id)\n",
    "print(job)\n",
    "results_df = None\n",
    "if len(job.result_files):\n",
    "    print(job.result_files)\n",
    "    results = client.files.retrieve_content(job.result_files[0])\n",
    "    with open('results.csv', 'w') as f:\n",
    "        f.write(results)\n",
    "    results_df = pd.read_csv('results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_mean_token_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.86116</td>\n",
       "      <td>0.44179</td>\n",
       "      <td>2.99447</td>\n",
       "      <td>0.11884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.78176</td>\n",
       "      <td>0.45173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.97844</td>\n",
       "      <td>0.43821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.86732</td>\n",
       "      <td>0.46739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.07915</td>\n",
       "      <td>0.44686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  train_loss  train_accuracy  valid_loss  valid_mean_token_accuracy\n",
       "0     1     2.86116         0.44179     2.99447                    0.11884\n",
       "1     2     2.78176         0.45173         NaN                        NaN\n",
       "2     3     2.97844         0.43821         NaN                        NaN\n",
       "3     4     2.86732         0.46739         NaN                        NaN\n",
       "4     5     3.07915         0.44686         NaN                        NaN"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the 'valid_loss' values from the 'results_df' DataFrame.\n",
    "# We drop any NaN values to ensure a smooth plot.\n",
    "plt.figure(figsize=(10, 6))  # Setting a suitable figure size for better visibility\n",
    "results_df['valid_loss'].dropna().plot(color=\"royalblue\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Loss Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Validation Loss', fontsize=12)\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n",
    "\n",
    "# Plotting the 'valid_mean_token_accuracy' values from the 'results_df' DataFrame.\n",
    "# We omit any NaN values to ensure a smooth, continuous plot.\n",
    "plt.figure(figsize=(10, 6))  # Designating an optimal figure size for better clarity\n",
    "results_df['valid_mean_token_accuracy'].dropna().plot(color=\"darkgreen\")\n",
    "\n",
    "# Adding titles and labels for clarity\n",
    "plt.title('Validation Mean Token Accuracy Over Time', fontsize=15)\n",
    "plt.xlabel('Epoch/Index', fontsize=12)\n",
    "plt.ylabel('Mean Token Accuracy', fontsize=12)\n",
    "\n",
    "# Showcasing the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# that mean token accuracy is interesting.. it claims to have a severely low accuracy but as we will see, this is not\n",
    "#  true according to our testing set.. I frankly am not sure why this reported accuracy is so low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evalauting our classifier using test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0',\n",
       " {'0': 0.8948484765359231,\n",
       "  '1': 0.06089519543934895,\n",
       "  '4': 0.023477189237092196,\n",
       "  '2': 0.01504005956544302,\n",
       "  '3': 0.004622934048641707})"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the numpy library to perform mathematical operations\n",
    "import numpy as np\n",
    "\n",
    "# Define a function to run the fine-tuned model and get the model's response\n",
    "def run_ft_model(review, ft_id, system='', chat=False):\n",
    "    \"\"\"\n",
    "    Given a review and a fine-tuned model ID, this function uses OpenAI's Completion API to\n",
    "    generate a completion. It also calculates the exponential of the top log probabilities for the completion.\n",
    "    \n",
    "    Parameters:\n",
    "    - review (str): The text of the review.\n",
    "    - ft_id (str): The ID of the fine-tuned model.\n",
    "    \n",
    "    Returns:\n",
    "    - str: The completion generated by the model.\n",
    "    - dict: A dictionary of tokens and their corresponding exponential of top log probabilities.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Use OpenAI's API to create a completion using the fine-tuned model\n",
    "    if chat:\n",
    "        completion = client.chat.completions.create(\n",
    "            model=ft_id,\n",
    "            messages=[\n",
    "                {\"role\": \"system\", \"content\": system},\n",
    "                {\"role\": \"user\", \"content\": review}\n",
    "            ],\n",
    "            max_tokens=1,\n",
    "            temperature=0.1\n",
    "        )\n",
    "        text = completion.choices[0].message.content.strip()\n",
    "        return text, None\n",
    "    else:\n",
    "        completion = client.completions.create(\n",
    "            model=ft_id,                   # Specify the fine-tuned model ID\n",
    "            prompt=f'{review}\\n###\\n',     # Format the review with the prompt structure\n",
    "            max_tokens=1,                  # Limit the response to 1 token (useful for classification tasks)\n",
    "            temperature=0.1,               # Set a low temperature for deterministic output\n",
    "            logprobs=5                     # Request the top 5 log probabilities for the completion\n",
    "        )\n",
    "        \n",
    "        # Extract the model's completion text and strip any extra whitespace\n",
    "        text = completion.choices[0].text.strip()\n",
    "        \n",
    "        # Convert the log probabilities to regular probabilities using the exponential function\n",
    "        # This provides a clearer understanding of the model's confidence in its responses\n",
    "        probs = {k: np.exp(v) for k, v in completion.choices[0].logprobs.top_logprobs[-1].items()}\n",
    "        \n",
    "        return text, probs\n",
    "\n",
    "# Example usage: Predict the sentiment and associated probabilities of the given review using the fine-tuned model\n",
    "run_ft_model('I hated this thing it was the worst', 'ft:babbage-002:personal::8EJokS4B')  # babbage for one epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As of 11/5/2023 - pricing for fine-tuning (https://openai.com/pricing)\n",
    "\n",
    "#    Model\t         Training          \tInput usage \t   Output usage\n",
    "# babbage-002\t$0.0004 / 1K tokens\t$0.0016 / 1K tokens\t$0.0016 / 1K tokens\n",
    "# davinci-002\t$0.0060 / 1K tokens\t$0.0120 / 1K tokens\t$0.0120 / 1K tokens\n",
    "# GPT-3.5 Turbo\t$0.0080 / 1K tokens\t$0.0120 / 1K tokens\t$0.0160 / 1K tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price to train was $1.1334228\n"
     ]
    }
   ],
   "source": [
    "train_price_usd = 0.0004 * job.trained_tokens / 1_000\n",
    "print(f'Price to train was ${train_price_usd}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price for a single inference (on avg): $2.7830718761390663e-05 and cost to run test: $1.6034112000000003\n"
     ]
    }
   ],
   "source": [
    "avg_tokens_per_input = job.trained_tokens / training_df.shape[0]\n",
    "# output is always 1 token\n",
    "\n",
    "avg_inference_price_usd = 0.0016 * (avg_tokens_per_input / 1_000) + (0.0016 / 1_000)\n",
    "avg_inference_price_usd\n",
    "print(f'Price for a single inference (on avg): ${avg_inference_price_usd} and cost to run test: ${avg_inference_price_usd * test_df.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stars_preds = []\n",
    "\n",
    "# Create a tqdm progress bar\n",
    "pbar = tqdm(list(range(test_df.shape[0])), total=test_df.shape[0], desc=\"Processing rows\")\n",
    "\n",
    "# Loop through the dataframe\n",
    "for index in pbar:\n",
    "    row = test_df.iloc[index]\n",
    "    model_pred, probs = run_ft_model(row.review, FINETUNED_MODEL_ID)\n",
    "    ground_truth = str(row.star).strip()\n",
    "    stars_preds.append(model_pred)\n",
    "    if index % 1_000 == 0:\n",
    "        print(index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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666/5hu4GDRpo4cKFcncv+gzDbdu2GZ83+enYsaPdF1UXL15Uz549lZiYqCZNmthd/e3bt68R1k+dOqUGDRpo4MCBslgsRuiuX79+sX43WrZsqQ8++EDu7u7KyMhQ27ZtjeH+R48eNUJ33lEQbdu21bRp0+Th4SGbzaY333xTq1atum7feT+nX3nlFeP8S9fmticlJemee+65bog/gL82QjcAmCTv1avCPM7ovvvu0xdffCHpWvhr2bKlmjVrpkaNGmncuHE3XU+nTp2KvW3eofHe3t7q27evEbqLM9/1Vvjyyy+Nn//+978bgVu6diWxd+/e+uCDDyRde7RUfqG7c+fOdsGkcePGRui+cuVKkWro2LGjEbila1+6vPrqq8Y869yrtcUJO/nJDWX5qV27tkaMGFFg+x/7QkpKinFFtlSpUnZB4s4771SbNm20ZMkS5eTkaOfOneratavi4uKMdVq2bGkEVEmqV6+eWrRooS1bthT6eHbv3m383L17dyNwS9fm4bdr1041atQo1PkrCccjSVWrVtW6dev0+eefa8OGDdq7d+919xk4dOiQ+vTpozVr1hQ4DSKvhg0bqmHDhsbrzMxMHTlyRLt27dLvv/9uLM87VzqvDh063LI+KF37nOvTp486d+583RXpGjVq2PXDnJwcHTt2THv37rWbRlNQrcXVp08f4xh9fHzUsGFDbd68WdL/fq8zMzPtPrtefPFFeXh4GMc0dOjQfEP3fffdZ/w8btw4/ec//9H999+vRo0a6R//+MctPbcAXAehGwBMkvcO1XfeeecN1+/Tp4927typ7777TpcvX9bGjRuNYOXv768OHTqof//+CgwMLHItvr6++V71KowKFSpc93ieGjVqGD/nffTVn7nVz9/O+2zj/Iaf5l32x+cg5/pjyClTpozxc2HqvVENef9Av3TpktLS0or97/BnSpUqJT8/PwUHB6tt27bq3bu3XWjNK7++kPdZ0JmZmXZ1/9Hx48clXbsHQa7q1atft17NmjWLFFLzPqO8SpUqdm1+fn5Fugt7STieXJ6enurcubM6d+4sq9Wq/fv36/vvv9eXX36pX375xXjvefPm/ekXJXlduHBBn376qf7zn//o4MGD+V6BL6j//vHcFsWECRPUqVMn/fjjjxo1apQOHTokm82mr7/+Wj169Mh3m6tXr2r16tXasmWL4uPj83004K3+bCjM73VaWppxXwPJ/jNNuvZUgTvuuMPuiwzp2pcW27Zt07p162SxWLR582Yj0Od+oTNgwIBbMlcegOsgdAOASXLnPUv5/xH/R97e3po7d662b9+uL774Qt99951SUlIkXbtyt3DhQq1bt06rVq1StWrVilTLzTw2Ku8fprnyDp3Mb660dP0f0vnt52YU9L658t5ErKD5697e3navi3qVqig13GoLFy4s0nzcXPn1hbz/nu7u7n/6xUB+x5TfUNrCjO4oyM1sK5WM49m+fbtSUlKUmpqqJ598UpUqVZKXl5eaNm2qpk2baujQofrHP/5hjKz44434CvLzzz/rmWeeMb7satCggZo2bapGjRrpww8/VEJCwp9uf7OPkPPy8lKDBg00Z84cPf7440pJSdHp06fVt29frV+/XhUrVjTWPX/+vHr27Kmff/5Z0v/msoeHh2vjxo365ptvbqqWghTm9/qP/+759YP8Pjfc3Nw0ceJEPfXUU9qwYYO+/fZbY1rCxYsXtXr1an322WdauHCh3V3dAfy1EboBwAQ2m83ujr6FvXO1m5ub6tevr4ceekjStT+wY2NjNX/+fP3444+6dOmS5s6dq7FjxxapnvzuwltYv//+u06fPq2goCBjWe4VOkl2w7rz/nH7x2G0+T2u52YEBQUZc+UPHz583dXMw4cPGz8X9UuKotSQ3/vlynuH4zvvvFPly5c3pY6iyK8vBAQEGD97eXnp22+/tfu3zM7ONobe5rdNbqjKqzA3D8zrrrvuMm7S9dNPP+lvf/ub0Xbx4kXNmDFD99xzj+655x67Yfz5KQnH869//cu4el+mTBk9++yzdu0eHh6KjIw0Qndhh1i///77RuB+5ZVX9OKLLxptuc/K/jM381mQV4UKFTR69GhjnnZKSopGjRqlmTNnGuvMnTvXOJc9evSw+9z697//fUvqKK7y5curVKlSxpeBP/30k92w/dybIBakVq1aevPNN1WqVCmdPn1a+/bt09KlSxUbG6vMzEzNnDlTc+fONf04ADgHJp4AgAk++ugj4+qHl5dXgUMvc2VnZ+u5555TkyZN9OCDD+o///mPpGtXyJ988kl17NjRWDfvMNy8QeLPriTf7ON48j4712q1at68ecbr3LtFS/bD6P9486hNmzYVuP+8x1HYK4p5n9P7wQcfGKMCpGt/QC9evNh43bZt20Lts6jy1vD5558rNjbWeH358mW9++67xus2bdrc0sciFVd+NQQFBRmPDcvIyLB73NLVq1fVtm1bPfzww3rppZd05MgRSdfmv+f67rvv7B4pt2/fPu3YsaNIdTVp0sT4edWqVXbTM9auXatFixZp9OjRGj9+vLG8oH5TEo4n94sz6drvT94vqqRrw5tXrlxpvK5Tp47xc94vBP74e523rryPO0tMTLRrK2jI9q3sg23btlW7du2M13mHWv9ZrWfPnrWbw5+31sJ+pt2sUqVKKTQ01Hg9e/ZsY4RDTk6OpkyZku92w4cP1wMPPKBmzZrp008/lXStv3Xq1Ml4CoFk/zkNAFzpBoCbcPHiReOPM5vNpitXrujAgQM6dOiQsc7LL79sdzU4Px4eHqpWrZpxdXzIkCFq3bq17rrrLp0+fdq4Q7ZkH3LzDhVNSEjQqFGj5Ovrq6ioqFtyfLlWr16tEydOqEGDBtq9e7dxx2g3Nze7PzTzBodvv/1Wo0eP1n333actW7b86V2P8x7H3LlzdfDgQf3tb39TmzZtCtymZ8+eWr58uc6fP69ff/1Vjz/+uFq2bKnMzExt2bLFmDvaoEEDPfbYY8U+9j/TtGlTRUZG6ocfflBWVpb69u2r1q1bG48My71ZVNmyZTVo0CBTarhVnnvuOeOGfWPHjtXWrVtVvXp1ff/998YXSNnZ2cbc18aNG6tu3bpKTExUZmamevXqpQ4dOshqtWrTpk1FHlrfvXt3zZ07V7///rt+/PFHPfbYY2rRooXS0tLsglzv3r2Nn/P2m9WrV+vSpUuqW7euevTo4fDjeeGFF7R+/XplZGTowoUL6tixo1q2bKnAwECdP39e27ZtM+YLe3h4qG/fvvke17lz54wnB0ycOFH+/v7G1JXx48frwIEDslgs+ve//20XUm/1zckK8uabb+r77783viQZP368mjdvLh8fH7vPvTlz5igpKUk+Pj7atGlTgTd9y3vsW7du1ZgxYxQQEKCXXnrpltf+zDPPGI9Z27x5s5566imFhoYqLi4u35Er0rWbFH722WeSpLffflvffvutqlWrptTUVLvh8nk/pwGAK90AcBMuX76sjz/+WB9//LHmzJmjZcuW2QXunj17FvqPxddee80YNmu1WvXVV19p/vz5dn9MR0REGHfDlqTQ0FBjuGhWVpZWrFhxy4dtNm/eXLVq1dL+/fu1aNEiu8dSDR482G5IZrt27YwrjNK1x4mNGjVK27ZtU69evQp8j7x/oP78889asWJFgX/05qpcubJmzJihSpUqSbo2f3TNmjXasGGDEbhr166tWbNmXTeU+FZxd3fXe++9Z9xELTMzU5s2bdKKFSuMwH3nnXdq1qxZN3UDq9uhV69ednc1//bbb7Vo0SLjRmN33HGHpk+fLi8vL0nXvnCZMmWKcQXzypUrWrVqlT777DNlZ2frmWeeKdL7V6xYUVOnTjVuAHf69GmtWLFCmzZtMq5AdurUSU888YSxTd5+k5KSoqVLlxpzox19PMHBwXr//fdVunRpSdf6xtdff62FCxdq48aNRugsVaqUxowZY3e39Jo1a9pdGV63bp0+++wzZWVlacCAAcbV6oyMDK1atUobNmyQxWKxe0RcfkPkzRAQEKBXXnnFeJ2UlGQ8NaB3797G+c3OztbGjRu1atUq/f777wXWmvff9MqVK1q6dGmRRxkU1qOPPqrOnTsbrw8dOqSlS5fq8OHDatWqld3Indwr8P3791eHDh2MY9q6davmz5+vjRs3Gp87NWvW1P/93/+ZUjMA50ToBoBbyNfXV8HBwXr88ce1ZMkSRUdHF3o4Z+nSpfXJJ59o/Pjxaty4sQICAuTp6ak77rhD4eHhGjlypBYsWGB3kyB/f3+9++67uvfee1WqVCmVL18+37to34yyZctqxYoV6tOnj/z9/eXt7a0GDRrovffes3vubu4xLF26VI8//rjKly8vX19fhYaGaurUqXrjjTcKfI9nn31WvXv3VoUKFeTl5aW7777bbr50QSIjI7VhwwYNHjxYdevWVenSpVW6dGnVr19fr732mlauXGk3V9cMlStX1ooVKzRmzBg1adJE5cqVk5eXl6pVq6a+fftqw4YNdkOXSyp3d3dNmTJFkydPVrNmzeyOo0ePHlq7dq3dFyySdO+992r16tXq1KmTypUrp9KlS+uBBx7QkiVL1LJlyyLX8NBDD2nt2rXq2rWrAgMDjbuyR0REaMKECdcN+W3Xrp2GDBmigIAAlSpVSoGBgcaV65JwPK1atdKXX36pF198UfXr19cdd9whDw8P3Xnnnapdu7b69Omjzz77zO6RZtK1KSkxMTEKCQlRqVKldOedd6pp06bKyMhQy5Yt9cknn6hx48YqW7asfHx8VLduXY0cOVLvv/++sY/bOWe6Z8+eCgsLM17PmzdPP//8s+677z6tWLFCLVq0UPny5eXl5aWaNWtqyJAhdkP+v/76a2MkQYMGDRQdHa3g4GCVKlVK/v7+diNobrV33nlHI0aMUK1ateTl5aUqVapoyJAhmjFjht16uV8GeXh46N1339WMGTPUvHlzValSRaVKlVKZMmVUv35941FjZjylAIDzcrOZeWtVAAAAoARat26dLl68qHLlyqlKlSp2N+g7deqU2rVrp5ycHJUpU0axsbEl4p4MAJwTc7oBAADwl7N7926tWbNG0rWREY888ogCAwOVlpambdu2GTd4a9myJYEbwE3hSjcAAAD+co4fP66nn376Tx8NVqFCBa1YsUJ33333bawMgKshdAMAAOAv6ddff9Unn3yiPXv26Ndff1VGRoZ8fHxUtWpVNW/eXM8999wNnz4BADdC6AYAAAAAwCTcvRwAAAAAAJMQugEAAAAAMAl3L78FcnJylJWVJXd3d+5uCQAAAAB/ATabTTk5OfL09JS7e8HXswndt0BWVpYSEhIcXQYAAAAA4DYLCQmRl5dXge2E7lsg91uNkJAQeXh4OLga15Wdna2EhATOM5wefRmuhP4MV0J/hqugL98euef5z65yS4TuWyJ3SLmHhwed+jbgPMNV0JfhSujPcCX0Z7gK+vLtcaMpxtxIDQAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAFyMj4+Po0vA/+fp6AIAAAAAoCTLyLIqMyfL0WUUSWCNYF3NtkjZjq6k8Eq5e8rH08vRZdxyhG4AAAAA+BOZOVmanfi5ktPTHF1KoVmtFnl5eTu6jEIL8C2nF+t2lI8I3QAAAADwl5Ocnqakq6mOLqPQMjIyGGJeQjCnGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwSYkN3QMHDtQbb7xhvD58+LCeeuophYaG6sknn9TBgwft1t+4caPatGmj0NBQDRo0SOfPnzfabDabpkyZombNmqlJkyaaNGmScnJyjPYLFy5oyJAhCg8PV+vWrbV+/XrzDxAAAAAA4PJKZOj+/PPPtW3bNuP11atXNXDgQEVGRmrNmjUKDw/Xiy++qKtXr0qSDhw4oJEjR2rw4MFasWKFLl26pKioKGP7Tz75RBs3blRMTIymT5+uDRs26JNPPjHao6Ki9Pvvv2vFihX6+9//rjfffFMHDhy4fQcMAAAAAHBJJS50p6WladKkSQoJCTGWffHFF/L29tZrr72mWrVqaeTIkSpTpoy++uorSdLixYvVoUMHdenSRXXr1tWkSZO0bds2nTp1SpK0cOFCDR06VJGRkWrWrJmGDRumJUuWSJJOnjyprVu36q233lLt2rX11FNP6fHHH9fSpUtv/8EDAAAAAFxKiQvdEydOVOfOnXXPPfcYy+Lj4xURESE3NzdJkpubmxo1aqS4uDijPTIy0lg/MDBQQUFBio+PV3Jyss6cOaPGjRsb7REREUpKStK5c+cUHx+vwMBAVa1a1a59//79Jh8pAAAAAMDVeTq6gLx27typH374QRs2bFB0dLSxPCUlxS6ES1LFihV19OhRSdK5c+dUuXLl69rPnj2rlJQUSbJrr1SpkiQZ7fltm5ycXOT6s7Ozi7wNCi/3/HKe4ezoy3Al9Ge4EvozXIXNZjP+n3vh0lk40+9fYWstMaHbYrFo9OjRGjVqlHx8fOza0tPT5eXlZbfMy8tLVqtVkpSRkVFge0ZGhvE6b5skWa3WG+67KBISEoq8DYqO8wxXQV+GK6E/w5XQn5GXj4+PAmsEy2q1GNnCWVgsFkeXUGhWT4sys7KUePQnpzvPN1JiQndMTIwaNGigFi1aXNfm7e19XQi2Wq1GOC+o3dfX1y5ge3t7Gz9Lkq+v7w33XRQhISHy8PAo8nYonOzsbCUkJHCe4fToy3Al9Ge4EvozCnI12yIvL+9iZQRHsNlsslgs8vb2dpor3V5e3irl6am6des6upRCy/3MuJESE7o///xzpaamKjw8XNL/gvGmTZvUqVMnpaam2q2fmppqDAsPCAjIt93f318BAQGSrg1Rz523nTvkPLe9oG2LysPDgw/o24DzDFdBX4YroT/DldCfcR3nGfEsSXb3wnI2rvi7V2JupLZo0SJt2LBB69at07p169S6dWu1bt1a69atU2hoqPbv3283N2Hfvn0KDQ2VJIWGhio2NtbY15kzZ3TmzBmFhoYqICBAQUFBdu2xsbEKCgpS5cqVFRYWpqSkJJ09e9auPSws7PYcOAAAAADAZZWYK91VqlSxe12mTBlJUrVq1VSxYkVNnTpVb7/9tp5++mktX75c6enp6tChgySpZ8+e6t27t8LCwhQSEqK3335bLVu2VHBwsNE+ZcoU3XXXXZKkqVOnqn///pKk4OBgNW/eXMOHD9fIkSOVkJCgjRs3avHixbfr0AEAAAAALqrEhO4/4+fnp9mzZ2v06NH69NNPVadOHX300UcqXbq0JCk8PFxjx47V9OnTdfHiRT344IMaN26csf2AAQP022+/afDgwfLw8FC3bt3Ur18/o33SpEkaOXKkunfvLn9/f40fP14NGza83YcJAAAAAHAxJTZ0v/POO3avGzZsqLVr1xa4fteuXdW1a9d82zw8PBQVFaWoqKh82ytWrKgPP/yw+MUCAAAAAJCPEjOnGwAAAAAAV0PoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMUqJC9y+//KIBAwYoPDxcLVu21Jw5c4y2t956S3Xq1LH7b/HixUb7xo0b1aZNG4WGhmrQoEE6f/680Waz2TRlyhQ1a9ZMTZo00aRJk5STk2O0X7hwQUOGDFF4eLhat26t9evX354DBgAAAAC4NE9HF5ArJydHAwcOVEhIiNauXatffvlFr7zyigICAvTYY4/p+PHjevXVV/XEE08Y2/j5+UmSDhw4oJEjR2rMmDGqW7eu3n77bUVFRWn27NmSpE8++UQbN25UTEyMsrKyNHz4cFWsWFEDBgyQJEVFRSkjI0MrVqxQfHy83nzzTdWoUUMNGza8/ScCAAAAAOAySkzoTk1NVb169RQdHS0/Pz9Vr15d999/v2JjY43QPWDAAPn7+1+37eLFi9WhQwd16dJFkjRp0iS1atVKp06dUnBwsBYuXKihQ4cqMjJSkjRs2DBNmzZNAwYM0MmTJ7V161Zt2bJFVatWVe3atRUXF6elS5cSugEAAAAAN6XEDC+vXLmy3n//ffn5+clmsyk2NlZ79+5VkyZNdPnyZSUnJ6t69er5bhsfH28EakkKDAxUUFCQ4uPjlZycrDNnzqhx48ZGe0REhJKSknTu3DnFx8crMDBQVatWtWvfv3+/accKAAAAAPhrKDFXuvNq3bq1Tp8+rVatWumRRx7RwYMH5ebmpg8//FDbt29XuXLl9NxzzxlDzc+dO6fKlSvb7aNixYo6e/asUlJSJMmuvVKlSpJktOe3bXJyspmHCAAAAAD4CyiRoXv69OlKTU1VdHS0JkyYoPr168vNzU01a9bUs88+q7179+pf//qX/Pz81LZtW2VkZMjLy8tuH15eXrJarcrIyDBe522TJKvVqvT09AK3Lars7Owib4PCyz2/nGc4O/oyXAn9Ga6E/gxXYbPZjP+7ubk5uJqicabfv8LWWiJDd0hIiCTJYrFo2LBh2rdvn1q1aqVy5cpJkurWrauff/5Zy5YtU9u2beXt7X1dSLZarfL19bUL2N7e3sbPkuTr61vgtj4+PkWuOyEhocjboOg4z3AV9GW4EvozXAn9GXn5+PgosEawrFaLcUHPWVgsFkeXUGhWT4sys7KUePQnpzvPN1JiQndqaqri4uLUpk0bY9k999yjzMxMXb58WRUqVLBbv2bNmtq1a5ckKSAgQKmpqdftz9/fXwEBAZKklJQUY9527pDz3PaCti2qkJAQeXh4FHk7FE52drYSEhI4z3B69GW4EvozXAn9GQW5mm2Rl5d3sS7MOYLNZpPFYpG3t7fTXOn28vJWKU9P1a1b19GlFFruZ8aNlJjQ/euvv2rw4MHatm2bEZQPHjyoChUqaNGiRdq/f7/mz59vrJ+YmKiaNWtKkkJDQxUbG6uuXbtKks6cOaMzZ84oNDRUAQEBCgoKUmxsrBG6Y2NjFRQUpMqVKyssLExJSUk6e/as7rrrLqM9LCysyMfg4eHBB/RtwHmGq6Avw5XQn+FK6M+4jvOMeJYkI2g7S+DOyxV/90rM3ctDQkJUv359jRgxQseOHdO2bds0efJkvfTSS2rVqpX27t2ruXPn6uTJk1q6dKnWrVun/v37S5J69uyp9evXa+XKlUpMTNRrr72mli1bKjg42GifMmWKdu/erd27d2vq1Knq06ePJCk4OFjNmzfX8OHDlZiYqJUrV2rjxo165plnHHYuAAAAAACuocRc6fbw8NCsWbM0btw49ejRQ76+vurdu7f69OkjNzc3TZs2TdOnT9e0adNUpUoVTZ06VeHh4ZKk8PBwjR07VtOnT9fFixf14IMPaty4cca+BwwYoN9++02DBw+Wh4eHunXrpn79+hntkyZN0siRI9W9e3f5+/tr/PjxPKMbAAAAAHDTSkzolq7NzY6Jicm3rU2bNnbzvf+oa9euxvDyP/Lw8FBUVJSioqLyba9YsaI+/PDDohcMAAAAAMCfKDHDywEAAAAAcDWEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJCUqdP/yyy8aMGCAwsPD1bJlS82ZM8doO3XqlPr166ewsDA9+uij2rFjh92233//vTp16qTQ0FD16dNHp06dsmufP3++WrRoofDwcI0YMULp6elGm8Vi0YgRIxQZGanmzZtr3rx55h4oAAAAAOAvocSE7pycHA0cOFDly5fX2rVrNWbMGH3wwQfasGGDbDabBg0apEqVKmn16tXq3LmzBg8erNOnT0uSTp8+rUGDBqlr165atWqVKlSooJdfflk2m02StGnTJsXExGjs2LFasGCB4uPjNXnyZOO9J02apIMHD2rBggUaPXq0YmJi9NVXXznkPAAAAAAAXIenowvIlZqaqnr16ik6Olp+fn6qXr267r//fsXGxqpSpUo6deqUli9frtKlS6tWrVrauXOnVq9erSFDhmjlypVq0KCB+vfvL0maMGGCHnzwQe3Zs0dNmzbVwoUL1bdvX7Vq1UqSNGbMGA0YMEDDhw+XzWbTypUr9fHHH6t+/fqqX7++jh49qiVLlqh9+/aOPCUAAAAAACdXYq50V65cWe+//778/Pxks9kUGxurvXv3qkmTJoqPj9d9992n0qVLG+tHREQoLi5OkhQfH6/IyEijzdfXV/Xr11dcXJyys7OVkJBg1x4WFqbMzEwlJiYqMTFRWVlZCg8Pt9t3fHy8cnJyzD9wAAAAAIDLKjGhO6/WrVurV69eCg8P1yOPPKKUlBRVrlzZbp2KFSvq7NmzkvSn7ZcuXZLFYrFr9/T0VLly5XT27FmlpKSofPny8vLyMtorVaoki8WitLQ08w4SAAAAAODySszw8rymT5+u1NRURUdHa8KECUpPT7cLxZLk5eUlq9UqSX/anpGRYbzOr91ms+XbJsnYf2FlZ2cXaX0UTe755TzD2dGX4Uroz3Al9Ge4itx7W9lsNrm5uTm4mqJxpt+/wtZaIkN3SEiIpGt3FR82bJiefPJJu7uNS9cCsY+PjyTJ29v7uoBstVpVtmxZeXt7G6//2O7r66vs7Ox82yQZ+y+shISEIq2P4uE8w1XQl+FK6M9wJfRn5OXj46PAGsGyWi3GBT1nYbFYHF1CoVk9LcrMylLi0Z+c7jzfSIkJ3ampqYqLi1ObNm2MZffcc48yMzPl7++vEydOXLd+7pDxgIAApaamXtder149lStXTt7e3kpNTVWtWrUkSVlZWUpLS5O/v79sNpsuXLigrKwseXpeOx0pKSny8fFR2bJli3QMISEh8vDwKPKxo3By5+dznuHs6MtwJfRnuBL6MwpyNdsiLy/vIl+UcxSbzSaLxSJvb2+nudLt5eWtUp6eqlu3rqNLKbTcz4wbKTGh+9dff9XgwYO1bds2BQQESJIOHjyoChUqKCIiQvPmzVNGRobR0WNjYxURESFJCg0NVWxsrLGv9PR0HT58WIMHD5a7u7tCQkIUGxurpk2bSpLi4uLkmecf1NPTU3FxccbN1mJjYxUSEiJ396JNeffw8OAD+jbgPMNV0JfhSujPcCX0Z1zHeUY8S5IRtJ0lcOflir97JeZGaiEhIapfv75GjBihY8eOadu2bZo8ebJeeuklNWnSRIGBgYqKitLRo0f10Ucf6cCBA+rWrZsk6cknn9S+ffv00Ucf6ejRo4qKilLVqlWNkN2rVy/NnTtXmzdv1oEDBxQdHa3u3bvL19dXvr6+6tKli6Kjo3XgwAFt3rxZ8+bNU58+fRx5OgAAAAAALqDEXOn28PDQrFmzNG7cOPXo0UO+vr7q3bu3+vTpIzc3N82aNUsjR45U165dVa1aNc2cOVNBQUGSpKpVq2rGjBkaP368Zs6cqfDwcM2cOdP4Zqdjx45KSkrSqFGjZLVa1a5dOw0fPtx476ioKEVHR6tv377y8/PTkCFD1K5dO4ecBwAAAACA6ygxoVu6Njc7JiYm37Zq1app8eLFBW770EMP6aGHHiqwfeDAgRo4cGC+bb6+vpo4caImTpxYtIIBAAAAAPgTJWZ4OQAAAAAArobQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJSlToTk5O1tChQ9WkSRO1aNFCEyZMkMVikSS99dZbqlOnjt1/ixcvNrbduHGj2rRpo9DQUA0aNEjnz5832mw2m6ZMmaJmzZqpSZMmmjRpknJycoz2CxcuaMiQIQoPD1fr1q21fv3623fQAAAAAACX5enoAnLZbDYNHTpUZcuW1ZIlS3Tx4kWNGDFC7u7uev3113X8+HG9+uqreuKJJ4xt/Pz8JEkHDhzQyJEjNWbMGNWtW1dvv/22oqKiNHv2bEnSJ598oo0bNyomJkZZWVkaPny4KlasqAEDBkiSoqKilJGRoRUrVig+Pl5vvvmmatSooYYNG97+EwEAAAAAcBklJnSfOHFCcXFx+u6771SpUiVJ0tChQzVx4kQjdA8YMED+/v7Xbbt48WJ16NBBXbp0kSRNmjRJrVq10qlTpxQcHKyFCxdq6NChioyMlCQNGzZM06ZN04ABA3Ty5Elt3bpVW7ZsUdWqVVW7dm3FxcVp6dKlhG4AAAAAwE0pMcPL/f39NWfOHCNw57p8+bIuX76s5ORkVa9ePd9t4+PjjUAtSYGBgQoKClJ8fLySk5N15swZNW7c2GiPiIhQUlKSzp07p/j4eAUGBqpq1ap27fv377+1BwgAAAAA+MspMVe6y5YtqxYtWhivc3JytHjxYjVr1kzHjx+Xm5ubPvzwQ23fvl3lypXTc889Zww1P3funCpXrmy3v4oVK+rs2bNKSUmRJLv23GCf257ftsnJyUU+huzs7CJvg8LLPb+cZzg7+jJcCf0ZroT+DFdhs9mM/7u5uTm4mqJxpt+/wtZaYkL3H02ePFmHDx/WqlWrdOjQIbm5ualmzZp69tlntXfvXv3rX/+Sn5+f2rZtq4yMDHl5edlt7+XlJavVqoyMDON13jZJslqtSk9PL3DbokpISCjyNig6zjNcBX0ZroT+DFdCf0ZePj4+CqwRLKvVYmQLZ5F7U2pnYPW0KDMrS4lHf3K683wjJTJ0T548WQsWLNB7772n2rVr695771WrVq1Urlw5SVLdunX1888/a9myZWrbtq28vb2vC8lWq1W+vr52Advb29v4WZJ8fX0L3NbHx6fIdYeEhMjDw6PI26FwsrOzlZCQwHmG06Mvw5XQn+FK6M8oyNVsi7y8vIuVERzBZrPJYrHI29vbaa50e3l5q5Snp+rWrevoUgot9zPjRkpc6B43bpyWLVumyZMn65FHHpEkubm5GYE7V82aNbVr1y5JUkBAgFJTU+3aU1NT5e/vr4CAAElSSkqKMW87d8h5bntB2xaVh4cHH9C3AecZroK+DFdCf4YroT/jOs4z4lmSjKDtLIE7L1f83SsxN1KTpJiYGC1fvlzvvvuuOnbsaCyfNm2a+vXrZ7duYmKiatasKUkKDQ1VbGys0XbmzBmdOXNGoaGhCggIUFBQkF17bGysgoKCVLlyZYWFhSkpKUlnz561aw8LCzPnIAEAAAAAfxkl5kr38ePHNWvWLA0cOFARERHG1WhJatWqlT766CPNnTtXbdu21Y4dO7Ru3TotXLhQktSzZ0/17t1bYWFhCgkJ0dtvv62WLVsqODjYaJ8yZYruuusuSdLUqVPVv39/SVJwcLCaN2+u4cOHa+TIkUpISNDGjRu1ePHi23wGAAAAAACupsSE7i1btig7O1sffPCBPvjgA7u2I0eOaNq0aZo+fbqmTZumKlWqaOrUqQoPD5ckhYeHa+zYsZo+fbouXryoBx98UOPGjTO2HzBggH777TcNHjxYHh4e6tatm92V80mTJmnkyJHq3r27/P39NX78eJ7RDQAAAAC4aW623PvJo9iys7MVFxensLAwl5yDUFJwnuEq6MtwJfRnuBL6Mwryu/Wqxu5foqSrqTdeuYTIyMhwmhu/SVKV0pU0KvwZ3eFV2tGlFFphPzNK1JxuAAAAAABcCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwSbFDd58+fbRz584C2zdv3qz27dsXd/cAAAAAADg9z8KumJ6ergsXLhiv9+zZo7Zt26patWrXrZuTk6MdO3bo9OnTt6ZKAAAAAACcUKFD9++//66OHTsqIyPDWDZ+/HiNHz++wG3CwsJuqjgAAAAAAJxZoUN35cqVNXLkSO3evVuStGHDBoWFhalq1arXrevh4aGKFSvq2WefvXWVAgAAAADgZAoduiWpW7du6tatmyQpNjZWzz//vB5++GFTCgMAAAAAwNkVKXTn9c0339zKOgAAAAAAcDnFDt2SZLPZtGvXLqWkpCgnJyffdbp06XIzbwEAAAAAgNMqduj+6aef9Pzzzxd4h3KbzSY3NzdCNwAAAADgL6vYoXvq1Kk6ffq0unTpogYNGsjLy+tW1gUAAAAAgNMrdujetWuXevbsqVGjRt3KegAAAAAAcBnuxd0wOztb9erVu5W1AAAAAADgUooduhs0aKBDhw7dyloAAAAAAHApxQ7d//jHP7R+/Xp98cUXys7OvpU1AQAAAADgEoo9p3v27NkqW7asXn31VUVFRenOO++Uu7t9hndzc9PWrVtvukgAAAAAAJxRsUP38ePH5eHhocDAwFtZDwAAAAAALqPYofubb765lXUAAAAAAOByij2nGwAAAAAA/LliX+mOiYm54Tpubm4aNGhQcd8CAAAAAACnZkrodnNzk81mI3QDAAAAAP7Sih26P/nkk+uWZWVlKTU1VZ999plSUlL0wQcf3FRxAAAAAAA4s2KH7vvvv7/Ati5duujZZ5/VggUL9Oabbxb3LQAAAAAAcGqm3EjNzc1NHTt21FdffWXG7gEAAAAAcAqm3b08PT1dv//+u1m7BwAAAACgxCv28PKcnJx8l1utVh08eFDz589XrVq1il0YAAAAAADOrtih+7777pObm1uB7TabTW+88UZxdw8AAAAAgNMrduhu3Lhx/jv09FTlypXVpUuXP73ZGgAAAAAArq7YoXvRokW3sg4AAAAAAFxOsUN3XufPn9evv/4qLy8v3XXXXSpXrtyt2C0AAAAAAE7tpkL3L7/8olGjRmnPnj3GMjc3NzVt2lSjRo1SjRo1brpAAAAAAACcVbFDd3Jysnr27Knz58+rcePGql27trKzs3X06FHt3LlTvXr10vr161W5cuVbWS8AAAAAAE6j2KE7JiZGly5d0pw5c9S8eXO7th07duill17S7Nmz9a9//eumiwQAAAAAwBm5F3fD7du3q3v37tcFbklq3ry5unfvrq1bt95UcQAAAAAAOLNih+7ffvtNderUKbC9du3aSklJKe7uAQAAAABwesUO3RUrVtTRo0cLbD927JjKly9f3N0DAAAAAOD0ih26W7RooU8//dTuzuW5du7cqU8//TTfoecAAAAAAPxVFPtGakOGDNHmzZvVt29fNW3aVPfcc48k6ejRo9qzZ4/Kli2rwYMH37JCAQAAAABwNsUO3QEBAVq6dKnGjBmj3bt3a9euXUZb48aNFR0draCgoFtSJAAAAAAAzqjYoVuSatasqQULFujChQs6deqUJKlq1aqqUKHCLSkOAAAAAABnVuw53XmVL19eDRs2VOXKleXn51fs/SQnJ2vo0KFq0qSJWrRooQkTJshisUiSTp06pX79+iksLEyPPvqoduzYYbft999/r06dOik0NFR9+vQxvgTINX/+fLVo0ULh4eEaMWKE0tPTjTaLxaIRI0YoMjJSzZs317x584p9DAAAAAAA5Cpy6F66dKk6duyo7Ozs69omT56sFi1aaOHChUUuxGazaejQoUpPT9eSJUv03nvvaevWrXr//fdls9k0aNAgVapUSatXr1bnzp01ePBgnT59WpJ0+vRpDRo0SF27dtWqVatUoUIFvfzyy7LZbJKkTZs2KSYmRmPHjtWCBQsUHx+vyZMnG+89adIkHTx4UAsWLNDo0aMVExOjr776qsjHAAAAAABAXkUaXv7OO+9o/vz58vT01C+//KKaNWvatZcpU0ZXr17VhAkT9NNPP2n06NGF3veJEycUFxen7777TpUqVZIkDR06VBMnTtTf/vY3nTp1SsuXL1fp0qVVq1Yt7dy5U6tXr9aQIUO0cuVKNWjQQP3795ckTZgwQQ8++KD27Nmjpk2bauHCherbt69atWolSRozZowGDBig4cOHy2azaeXKlfr4449Vv3591a9fX0ePHtWSJUvUvn37opweAAAAAADsFPpK95YtWzR//nzdf//9+vrrr68L3JI0duxYbdmyRaGhoVq+fLm+/fbbQhfi7++vOXPmGIE71+XLlxUfH6/77rtPpUuXNpZHREQoLi5OkhQfH6/IyEijzdfXV/Xr11dcXJyys7OVkJBg1x4WFqbMzEwlJiYqMTFRWVlZCg8Pt9t3fHy8cnJyCl0/AAAAAAB/VOjQvWzZMgUHB+ujjz5SYGBggetVrlxZc+bMUYUKFbRkyZJCF1K2bFm1aNHCeJ2Tk6PFixerWbNmSklJUeXKle3Wr1ixos6ePStJf9p+6dIlWSwWu3ZPT0+VK1dOZ8+eVUpKisqXLy8vLy+jvVKlSrJYLEpLSyt0/QAAAAAA/FGhh5cfPHhQzz77rEqVKnXDdf38/NS5c2etXbu22IVNnjxZhw8f1qpVqzR//ny7UCxJXl5eslqtkqT09PQC2zMyMozX+bXbbLZ82yQZ+y+s/Oa549bJPb+cZzg7+jJcCf0ZroT+DFeRe28rm80mNzc3B1dTNM70+1fYWgsduq9cuaK77rqr0AXUqFFDly9fLvT6eU2ePFkLFizQe++9p9q1a8vb2/u6q85Wq1U+Pj6SJG9v7+sCstVqVdmyZeXt7W28/mO7r6+vsrOz822TZOy/sBISEoq0PoqH8wxXQV+GK6E/w5XQn5GXj4+PAmsEy2q1GBf0nEXuk6CcgdXTosysLCUe/cnpzvONFDp0+/v769y5c4Xe8fnz56+bn10Y48aN07JlyzR58mQ98sgjkqSAgAAdO3bMbr3U1FRjyHhAQIBSU1Ova69Xr57KlSsnb29vpaamqlatWpKkrKwspaWlyd/fXzabTRcuXFBWVpY8Pa+djpSUFPn4+Khs2bJFqj0kJEQeHh5FPmYUTu78fM4znB19Ga6E/gxXQn9GQa5mW+Tl5V3ki3KOYrPZZLFY5O3t7TRXur28vFXK01N169Z1dCmFlvuZcSOFDt0NGjTQ5s2b9fLLLxdq/U2bNql69eqF3b0kKSYmRsuXL9e7775rd+fw0NBQffTRR8rIyDA6emxsrCIiIoz22NhYY/309HQdPnxYgwcPlru7u0JCQhQbG6umTZtKkuLi4uSZ5x/U09NTcXFxxs3WYmNjFRISInf3oj1RzcPDgw/o24DzDFdBX4YroT/DldCfcR3nGfEsSUbQdpbAnZcr/u4VOlU+8cQTOnz4sObMmXPDdefMmaP//ve/6tKlS6ELOX78uGbNmqUXXnhBERERSklJMf5r0qSJAgMDFRUVpaNHj+qjjz7SgQMH1K1bN0nSk08+qX379umjjz7S0aNHFRUVpapVqxohu1evXpo7d642b96sAwcOKDo6Wt27d5evr698fX3VpUsXRUdH68CBA9q8ebPmzZunPn36FLp2AAAAAADyU+gr3a1atVK7du00depUJSQkqHfv3goNDTVurJaZman9+/dr8eLF+vrrr9W4cWN17Nix0IVs2bJF2dnZ+uCDD/TBBx/YtR05ckSzZs3SyJEj1bVrV1WrVk0zZ85UUFCQJKlq1aqaMWOGxo8fr5kzZyo8PFwzZ840vtnp2LGjkpKSNGrUKFmtVrVr107Dhw839h8VFaXo6Gj17dtXfn5+GjJkiNq1a1fo2gEAAAAAyI+bLffWdoVw5coVjRgxQps2bZKbm5s8PDxUrlw55eTk6OLFi8rJyZHNZlPbtm01fvx43XHHHWbWXmJkZ2crLi5OYWFhLjkcoqTgPMNV0JfhSujPcCX0ZxTkd+tVjd2/RElXU2+8cgmRd2quM6hSupJGhT+jO7xKO7qUQivsZ0ahr3RLUpkyZTRt2jTt2LFD69atU0JCgs6dOycPDw9Vr15djRs3VqdOnYy50QAAAAAA/JUVKXTnat68uZo3b36rawEAAAAAwKUU7fbcAAAAAACg0AjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASUpk6LZarerUqZN2795tLHvrrbdUp04du/8WL15stG/cuFFt2rRRaGioBg0apPPnzxttNptNU6ZMUbNmzdSkSRNNmjRJOTk5RvuFCxc0ZMgQhYeHq3Xr1lq/fv3tOVAAAAAAgEvzdHQBf2SxWPTqq6/q6NGjdsuPHz+uV199VU888YSxzM/PT5J04MABjRw5UmPGjFHdunX19ttvKyoqSrNnz5YkffLJJ9q4caNiYmKUlZWl4cOHq2LFihowYIAkKSoqShkZGVqxYoXi4+P15ptvqkaNGmrYsOFtOmoAAAAAgCsqUaH72LFjevXVV2Wz2a5rO378uAYMGCB/f//r2hYvXqwOHTqoS5cukqRJkyapVatWOnXqlIKDg7Vw4UINHTpUkZGRkqRhw4Zp2rRpGjBggE6ePKmtW7dqy5Ytqlq1qmrXrq24uDgtXbqU0A0AAAAAuCklanj5nj171LRpU61YscJu+eXLl5WcnKzq1avnu118fLwRqCUpMDBQQUFBio+PV3Jyss6cOaPGjRsb7REREUpKStK5c+cUHx+vwMBAVa1a1a59//79t/bgAAAAAAB/OSXqSnevXr3yXX78+HG5ubnpww8/1Pbt21WuXDk999xzxlDzc+fOqXLlynbbVKxYUWfPnlVKSook2bVXqlRJkoz2/LZNTk6+ZccFAAAAAPhrKlGhuyAnTpyQm5ubatasqWeffVZ79+7Vv/71L/n5+alt27bKyMiQl5eX3TZeXl6yWq3KyMgwXudtk67dsC09Pb3AbYsqOzu7yNug8HLPL+cZzo6+DFdCf4YroT/DVeRO17XZbHJzc3NwNUXjTL9/ha3VKUJ3ly5d1KpVK5UrV06SVLduXf38889atmyZ2rZtK29v7+tCstVqla+vr13A9vb2Nn6WJF9f3wK39fHxKXKdCQkJRd4GRcd5hqugL8OV0J/hSujPyMvHx0eBNYJltVqMC3rOwmKxOLqEQrN6WpSZlaXEoz853Xm+EacI3W5ubkbgzlWzZk3t2rVLkhQQEKDU1FS79tTUVPn7+ysgIECSlJKSYszbzh1yntte0LZFFRISIg8PjyJvh8LJzs5WQkIC5xlOj74MV0J/hiuhP6MgV7Mt8vLyLtaFOUew2WyyWCzy9vZ2mivdXl7eKuXpqbp16zq6lELL/cy4EacI3dOmTdP+/fs1f/58Y1liYqJq1qwpSQoNDVVsbKy6du0qSTpz5ozOnDmj0NBQBQQEKCgoSLGxsUbojo2NVVBQkCpXrqywsDAlJSXp7Nmzuuuuu4z2sLCwItfp4eHBB/RtwHmGq6Avw5XQn+FK6M+4jvOMeJYkI2g7S+DOyxV/90rU3csL0qpVK+3du1dz587VyZMntXTpUq1bt079+/eXJPXs2VPr16/XypUrlZiYqNdee00tW7ZUcHCw0T5lyhTt3r1bu3fv1tSpU9WnTx9JUnBwsJo3b67hw4crMTFRK1eu1MaNG/XMM8847HgBAAAAAK7BKa50N2zYUNOmTdP06dM1bdo0ValSRVOnTlV4eLgkKTw8XGPHjtX06dN18eJFPfjggxo3bpyx/YABA/Tbb79p8ODB8vDwULdu3dSvXz+jfdKkSRo5cqS6d+8uf39/jR8/nmd0AwAAAABuWokN3UeOHLF73aZNG7Vp06bA9bt27WoML/8jDw8PRUVFKSoqKt/2ihUr6sMPPyx+sQAAAAAA5MMphpcDAAAAAOCMCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmKREhm6r1apOnTpp9+7dxrJTp06pX79+CgsL06OPPqodO3bYbfP999+rU6dOCg0NVZ8+fXTq1Cm79vnz56tFixYKDw/XiBEjlJ6ebrRZLBaNGDFCkZGRat68uebNm2fuAQIAAAAA/hJKXOi2WCx65ZVXdPToUWOZzWbToEGDVKlSJa1evVqdO3fW4MGDdfr0aUnS6dOnNWjQIHXt2lWrVq1ShQoV9PLLL8tms0mSNm3apJiYGI0dO1YLFixQfHy8Jk+ebOx/0qRJOnjwoBYsWKDRo0crJiZGX3311e09cAAAAACAyylRofvYsWPq3r27Tp48abd8165dOnXqlMaOHatatWrpxRdfVFhYmFavXi1JWrlypRo0aKD+/fvr3nvv1YQJE5SUlKQ9e/ZIkhYuXKi+ffuqVatWatiwocaMGaPVq1crPT1dV69e1cqVKzVy5EjVr19fbdu21fPPP68lS5bc9uMHAAAAALiWEhW69+zZo6ZNm2rFihV2y+Pj43XfffepdOnSxrKIiAjFxcUZ7ZGRkUabr6+v6tevr7i4OGVnZyshIcGuPSwsTJmZmUpMTFRiYqKysrIUHh5ut+/4+Hjl5OSYdKQAAAAAgL8CT0cXkFevXr3yXZ6SkqLKlSvbLatYsaLOnj17w/ZLly7JYrHYtXt6eqpcuXI6e/as3N3dVb58eXl5eRntlSpVksViUVpamipUqFDo+rOzswu9Loou9/xynuHs6MtwJfRnuBL6M1xF7jRbm80mNzc3B1dTNM70+1fYWktU6C5Ienq6XSiWJC8vL1mt1hu2Z2RkGK/za7fZbPm2STL2X1gJCQlFWh/Fw3mGq6Avw5XQn+FK6M/Iy8fHR4E1gmW1Woxs4SwsFoujSyg0q6dFmVlZSjz6k9Od5xtxitDt7e2ttLQ0u2VWq1U+Pj5G+x8DstVqVdmyZeXt7W28/mO7r6+vsrOz822TZOy/sEJCQuTh4VGkbVB4uVMFOM9wdvRluBL6M1wJ/RkFuZptkZeXd5HzgaPYbDZZLBZ5e3s7zZVuLy9vlfL0VN26dR1dSqHlfmbciFOE7oCAAB07dsxuWWpqqjFkPCAgQKmpqde116tXT+XKlZO3t7dSU1NVq1YtSVJWVpbS0tLk7+8vm82mCxcuKCsrS56e105HSkqKfHx8VLZs2SLV6eHhwQf0bcB5hqugL8OV0J/hSujPuI7zjHiWJCNoO0vgzssVf/dK1I3UChIaGqpDhw7ZDTOIjY1VaGio0R4bG2u0paen6/DhwwoNDZW7u7tCQkLs2uPi4uT5/79FqVevnjw9PY2bsuXuOyQkRO7uTnF6AAAAAAAllFOkyiZNmigwMFBRUVE6evSoPvroIx04cEDdunWTJD355JPat2+fPvroIx09elRRUVGqWrWqmjZtKunaDdrmzp2rzZs368CBA4qOjlb37t3l6+srX19fdenSRdHR0Tpw4IA2b96sefPmqU+fPo48ZAAAAACAC3CK4eUeHh6aNWuWRo4cqa5du6patWqaOXOmgoKCJElVq1bVjBkzNH78eM2cOVPh4eGaOXOmMZyiY8eOSkpK0qhRo2S1WtWuXTsNHz7c2H9UVJSio6PVt29f+fn5aciQIWrXrp1DjhUAAAAA4DpKbOg+cuSI3etq1app8eLFBa7/0EMP6aGHHiqwfeDAgRo4cGC+bb6+vpo4caImTpxYvGIBAAAAAMiHUwwvBwAAAADAGRG6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRtOxcfHx9ElAAAAAECheTq6ADhORpZVmTlZji6jSAJrBOtqtkXKdnQlhVfK3VM+nl6OLgMAAACAAxC6/8Iyc7I0O/FzJaenObqUQrNaLfLy8nZ0GYUW4FtOL9btKB8RugEAAIC/IkL3X1xyepqSrqY6uoxCy8jIYIg5AAAAAKfBnG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAEziVKH766+/Vp06dez+Gzp0qCTp8OHDeuqppxQaGqonn3xSBw8etNt248aNatOmjUJDQzVo0CCdP3/eaLPZbJoyZYqaNWumJk2aaNKkScrJybmtxwYAAAAAcD1OFbqPHTumVq1aaceOHcZ/b731lq5evaqBAwcqMjJSa9asUXh4uF588UVdvXpVknTgwAGNHDlSgwcP1ooVK3Tp0iVFRUUZ+/3kk0+0ceNGxcTEaPr06dqwYYM++eQTRx0mAAAAAMBFOFXoPn78uGrXri1/f3/jv7Jly+qLL76Qt7e3XnvtNdWqVUsjR45UmTJl9NVXX0mSFi9erA4dOqhLly6qW7euJk2apG3btunUqVOSpIULF2ro0KGKjIxUs2bNNGzYMC1ZssSRhwoAAAAAcAFOF7qrV69+3fL4+HhFRETIzc1NkuTm5qZGjRopLi7OaI+MjDTWDwwMVFBQkOLj45WcnKwzZ86ocePGRntERISSkpJ07tw5U48HAAAAAODaPB1dQGHZbDb99NNP2rFjh2bPnq3s7Gy1b99eQ4cOVUpKiu655x679StWrKijR49Kks6dO6fKlStf13727FmlpKRIkl17pUqVJElnz569brs/k52dXaxjQ+HYbDbj/7lfsDgL+gbyyu0P9Au4AvozXAn9Ga6Cv5tvj8LW6jSh+/Tp00pPT5eXl5fef/99/frrr3rrrbeUkZFhLM/Ly8tLVqtVkpSRkVFge0ZGhvE6b5skY/vCSkhIKPJxOYqPj48CawTLarUY58BZWCwWR5dQaFZPizKzspR49CenO88wnzN9ZgA3Qn+GK6E/Iy/+br49XPnvZqcJ3VWqVNHu3bt15513ys3NTfXq1VNOTo6GDx+uJk2aXBeQrVarfHx8JEne3t75tvv6+toFbG9vb+NnSfL19S1SjSEhIfLw8CjW8TnC1WyLvLy8jfNU0tlsNlksFnl7ezvNN3ZeXt4q5empunXrOroUlCDZ2dlKSEhwus8MID/0Z7gS+jMKwt/N5nPGv5tzPzNuxGlCtySVK1fO7nWtWrVksVjk7++v1NRUu7bU1FRjaHhAQEC+7f7+/goICJAkpaSkqGrVqsbPkuTv71+k+jw8PJzrA9p5Rm5Ikt2cfWfjVP0Ct43TfWYAf4L+DFdCf8Z1+Lv5tnHF3z2nuZHat99+q6ZNmyo9Pd1Y9t///lflypVTRESE9u/fbzd3Yd++fQoNDZUkhYaGKjY21tjuzJkzOnPmjEJDQxUQEKCgoCC79tjYWAUFBRVpPjcAAAAAAH/kNKE7PDxc3t7eevPNN3XixAlt27ZNkyZN0vPPP6/27dvr0qVLevvtt3Xs2DG9/fbbSk9PV4cOHSRJPXv21Pr167Vy5UolJibqtddeU8uWLRUcHGy0T5kyRbt379bu3bs1depU9enTx5GHCwAAAABwAU4zvNzPz09z587V+PHj9eSTT6pMmTJ6+umn9fzzz8vNzU2zZ8/W6NGj9emnn6pOnTr66KOPVLp0aUnXAvvYsWM1ffp0Xbx4UQ8++KDGjRtn7HvAgAH67bffNHjwYHl4eKhbt27q16+fg44UAAAAAOAqnCZ0S9K9996rTz75JN+2hg0bau3atQVu27VrV3Xt2jXfNg8PD0VFRSkqKuqW1AkAAAAAgOREw8sBAAAAAHA2hG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAN8XHx8fRJQAAUGJ5OroAAADwPxlZVmXmZDm6jCIJrBGsq9kWKdvRlRReKXdP+Xh6OboMAMBfAKEbAIASJDMnS7MTP1dyepqjSyk0q9UiLy9vR5dRaAG+5fRi3Y7yEaEbAGA+QjcAACVMcnqakq6mOrqMQsvIyGCIOQAABWBONwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAADw/3FTQAC3GncvBwAAgCl47vztwXPngZKN0A0AAABT8Nx58/HceaDkI3QDAADANDx3HsBfHXO6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAcwMfHx9ElAAAA4DbwdHQBAHArZGRZlZmT5egyCi2wRrCuZlukbEdXUnil3D3l4+nl6DIAAACcCqEbgEvIzMnS7MTPlZye5uhSCsVqtcjLy9vRZRRagG85vVi3o3xE6AYAACgKQjcAl5Gcnqakq6mOLqNQMjIyGGIOAADwF8CcbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJITu/89isWjEiBGKjIxU8+bNNW/ePEeXBAAAAABwcp6OLqCkmDRpkg4ePKgFCxbo9OnTev311xUUFKT27ds7ujQAAAAAgJMidEu6evWqVq5cqY8//lj169dX/fr1dfToUS1ZsoTQDQAAAAAoNoaXS0pMTFRWVpbCw8ONZREREYqPj1dOTo4DKwMAAAAAODNCt6SUlBSVL19eXl5exrJKlSrJYrEoLS3NcYUBAAAAAJwaw8slpaen2wVuScZrq9V6w+1tNpuxroeHx60v0CTZ2dkK8ikvT6f57sWmTM9MlfIqJcnN0cUUSmWfO5WdnV2ofoSb41z9mb6MgjlXX5boz/gz9Gfz0Z9vD/qy+ZyxL2dnZ0v6Xx4sCKFbkre393X/uLmvfXx8brh97hD0w4cP3/riTPY31XCuXuBMtUpSlnT88I+OruIvw6n6s7PUmYu+fFs5VV+WnKtWif58m9GfTUZ/vm3oyyZz4r58oynJzvZPYYqAgABduHBBWVlZ8vS8dkpSUlLk4+OjsmXL3nB7T09PhYSEyN3dXW5uzvFNEgAAAACg+Gw2m3JycowMWRBCt6R69erJ09NTcXFxioyMlCTFxsYaQfpG3N3drxueDgAAAACAs0xKMJWvr6+6dOmi6OhoHThwQJs3b9a8efPUp08fR5cGAAAAAHBibrYbzfr+i0hPT1d0dLT+/e9/y8/PTwMGDFC/fv0cXRYAAAAAwIkRugEAAAAAMAnDywEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbTsFisWjEiBGKjIxU8+bNNW/ePEeXBNwUq9WqTp06affu3Y4uBSi25ORkDR06VE2aNFGLFi00YcIEWSwWR5cFFNkvv/yiAQMGKDw8XC1bttScOXMcXRJwSwwcOFBvvPGGo8v4y+M53XAKkyZN0sGDB7VgwQKdPn1ar7/+uoKCgtS+fXtHlwYUmcVi0auvvqqjR486uhSg2Gw2m4YOHaqyZctqyZIlunjxokaMGCF3d3e9/vrrji4PKLScnBwNHDhQISEhWrt2rX755Re98sorCggI0GOPPebo8oBi+/zzz7Vt2zY98cQTji7lL48r3Sjxrl69qpUrV2rkyJGqX7++2rZtq+eff15LlixxdGlAkR07dkzdu3fXyZMnHV0KcFNOnDihuLg4TZgwQffee68iIyM1dOhQbdy40dGlAUWSmpqqevXqKTo6WtWrV9dDDz2k+++/X7GxsY4uDSi2tLQ0TZo0SSEhIY4uBSJ0wwkkJiYqKytL4eHhxrKIiAjFx8crJyfHgZUBRbdnzx41bdpUK1ascHQpwE3x9/fXnDlzVKlSJbvlly9fdlBFQPFUrlxZ77//vvz8/GSz2RQbG6u9e/eqSZMmji4NKLaJEyeqc+fOuueeexxdCsTwcjiBlJQUlS9fXl5eXsaySpUqyWKxKC0tTRUqVHBgdUDR9OrVy9ElALdE2bJl1aJFC+N1Tk6OFi9erGbNmjmwKuDmtG7dWqdPn1arVq30yCOPOLocoFh27typH374QRs2bFB0dLSjy4G40g0nkJ6ebhe4JRmvrVarI0oCAPzB5MmTdfjwYf3zn/90dClAsU2fPl0ffvih/vvf/2rChAmOLgcoMovFotGjR2vUqFHy8fFxdDn4/7jSjRLP29v7unCd+5oPEwBwvMmTJ2vBggV67733VLt2bUeXAxRb7vxXi8WiYcOG6bXXXrvui3+gJIuJiVGDBg3sRiLB8QjdKPECAgJ04cIFZWVlydPzWpdNSUmRj4+PypYt6+DqAOCvbdy4cVq2bJkmT57McFw4pdTUVMXFxalNmzbGsnvuuUeZmZm6fPky09jgVD7//HOlpqYa90LKvVC1adMm7d+/35Gl/aURulHi1atXT56enoqLi1NkZKQkKTY2ViEhIXJ3Z4YEADhKTEyMli9frnfffZdHOMJp/frrrxo8eLC2bdumgIAASdLBgwdVoUIFAjeczqJFi5SVlWW8njJliiRp2LBhjioJInTDCfj6+qpLly6Kjo7W+PHjde7cOc2bN4+5VgDgQMePH9esWbM0cOBARUREKCUlxWjz9/d3YGVA0YSEhKh+/foaMWKEoqKilJSUpMmTJ+ull15ydGlAkVWpUsXudZkyZSRJ1apVc0Q5+P8I3XAKUVFRio6OVt++feXn56chQ4aoXbt2ji4LAP6ytmzZouzsbH3wwQf64IMP7NqOHDnioKqAovPw8NCsWbM0btw49ejRQ76+vurdu7f69Onj6NIAuAg3m81mc3QRAAAAAAC4IibEAgAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAm8XR0AQAAQMrJydHq1av12Wef6ccff9SVK1dUrlw5NWzYUE899ZRatWp13TYWi0UXLlzQXXfdZXp9a9asUVRUVL5tpUuXVqVKlXT//fdryJAh8vf3L/b7/PGYct/3rbfe0lNPPVXs/QIA4ChuNpvN5ugiAAD4K8vJydHgwYP1zTff6KGHHlKTJk1UtmxZJScna/369Tp58qR69+6tN99809gmISFBQ4YM0dChQ9W1a1fTa8wNv23btlXbtm3t2n777Tdt375dO3fuVPXq1bVmzRqVKVOmyO+R3zGdOnVK+/btU3h4uO6+++5bciwAANxOXOkGAMDBNm3apC1btmjo0KEaNGiQXdvAgQPVu3dvLVq0SI8++qgaNWokSUpMTNSZM2due6116tRR586dr1vev39/vf7661q3bp1WrVqlvn37Fnnf+R1TcHCwgoODi10vAACOxpxuAAAc7IcffpCkfIeQe3l5qX///pKkvXv33ta6iurpp5+W9L/jAQAAhG4AABzOz89PkrRs2TJlZWVd1962bVsdOnRIL774oiTpjTfeMIaaR0VFqU6dOsa6586d09tvv61HHnlEDRs2VMOGDfXoo49q5syZdvueMWOG6tSpo2+++UaPPvqoGjRooJ49e97UcZQuXTrf5YcOHdIrr7yiv/3tb2rQoIEaNWqkp59+Wl988YWxTkHHtGbNGtWpU0crV6401q1Tp45GjRqlf//733ryySfVsGFDNW3aVK+99prOnTtn9942m00LFixQx44d1bBhQz388MP6+OOPNXPmTNWpU0e//vrrTR0zAAA3wvByAAAcrGvXrlq4cKE+/fRTbd26Va1bt1ZkZKQiIiJUpUoVubu7y939f9+T9+jRQx4eHlq1apV69OihiIgISdLvv/+uHj166NKlS+rVq5fuvvtupaWlad26dZo+fboyMjL06quv2r33sGHD9OSTT+qZZ55RqVKlbuo4tmzZIklq0KCBsSwuLk7PPvusgoKC9Oyzz6p8+fL69ddftXz5cv3zn//UHXfcoRYtWhR4TAXZsWOHNmzYoKefflo9evTQrl27tH79ep06dUrLli0z1hs9erRWrFihpk2bqmfPnjp9+rRmzpwpb2/vmzpWAAAKi9ANAICDVatWTfPmzVNUVJR++uknrVixQitWrDDaOnbsqAEDBhhXxMPDw3Xs2DGtWrVKYWFhxhzrtWvX6vTp05o+fboeeeQRY/89evTQAw88oK1bt14Xutu0aaORI0cWutb09HSdP3/eeG2z2XT+/Hl98803mjVrlgIDA/Xss88a7XPmzJGbm5sWLVqkgIAAY3mjRo00cOBAbd26VS1atCjwmAqSlJSkZcuWGXPcu3fvrpSUFO3Zs0c///yzqlevrgMHDmjFihVq3bq1Zs2aJTc3N0nSww8/rGeeeabQxwwAwM0gdAMAUAKEh4friy++UGxsrLZv3669e/fq4MGD+uWXXzRr1iytX79eixcvVlBQUIH76NOnjzp27Kjy5cvbLT9//rzuuOMOXb58+bptHnjggSLVOXfuXM2dO/e65aVKlVKbNm302muv2d25fPr06bpw4YIqVqxoLMvKylJOTo4k5VtTYVStWtUI3LkaNGigPXv2KDU1VdWrV9eXX34p6drN6HIDtyRFRETogQce0HfffVes9wYAoCgI3QAAlBDu7u5q3LixGjduLEm6cuWKtm3bplmzZuno0aMaP368YmJibriPefPmKT4+XidPntSpU6d05coVSbK70pwrbxgujM6dO6tLly6SpLS0NH366afatWuX+vTpo1deeUWenvZ/Wri7u+vixYuaN2+ejhw5oqSkJJ06dcqYX54bvosqv2eBe3l5SZKys7MlST/99JMkqUaNGtetW6tWLUI3AOC2IHQDAOBAV69e1ezZsxUQEKBevXrZtZUpU0aPPvqomjdvrocfflg7d+78030dOHBA/fv3V1ZWlpo1a6YWLVro3nvvVaNGjdS7d+98A66Hh0eR6g0ODra7Ot6hQwe98cYbmjt3rk6fPq333nvP7qrysmXLNGbMGFWqVElNmzZVeHi46tSpo4CAAHXr1q1I751X3vcoSGZmpqT/hfG8fHx8iv3eAAAUBaEbAAAH8vHx0fz583XHHXfoqaeeyvdmZmXLllVQUJB+++23P93Xu+++q8uXL+uzzz5T7dq1jeWZmZm6cOGC7rzzzltev5ubm8aNG6cff/xRX375perVq2fcZd1isWjSpEkKDg7W2rVrjTnpkhQbG3vLa/mjGjVqaMeOHTpx4oTdzd0k6cSJE6a/PwAAEo8MAwDAodzd3fXkk08qJSVFEydOzPeRYXv37tWPP/6o9u3b220n2Q/PvnDhgnx8fFStWjW77RctWqSMjAxj2PWt5uXlpSlTpsjb21szZszQ4cOHJUkZGRm6evWqgoKC7AJ3VlaW5s2bJ0l2NeV3TDejQ4cOkqQFCxbIZrMZy48ePart27ffkvcAAOBGuNINAICDDRs2TD/++KMWLVqkb7/9Vu3bt1fVqlVltVq1b98+ffXVV6pXr57+7//+z9imUqVKkqTPPvtMNptNXbp00cMPP6yZM2fqueeeU6dOnWSz2bR9+3b95z//kY+Pjy5fviybzVaoodlFVatWLb3yyiuaMGGCXnvtNa1Zs0Z33nmnGjdurF27dun1119XZGSk0tLStGHDBp04cULu7u66dOnSnx7TzYiIiFDXrl21Zs0apaam6uGHH1ZKSooWL15sBHwzzgUAAHkRugEAcLDSpUtr4cKFWrdunb788kutXr1aaWlp8vHxUa1atTR8+HD16tXLbm7yAw88oE6dOmnLli1KSEhQZGSkXn75ZXl4eGjdunWaMGGC7rzzTtWoUUMzZ85UQkKCPvzwQ+3Zs0dNmzY15Tj69u2rb775Rrt379Z7772n119/Xe+//76mTp2qHTt26IsvvpC/v78aNGigSZMmKTo6Wvv27dOVK1dUpkyZfI/pZo0bN07VqlXT6tWr9c477yggIEBDhgxRXFycvvzyy3znewMAcCu52fKOtwIAAHARly9flru7u0qXLn1dW//+/bV7927Fx8dfd8d1AABuJeZ0AwAAl7R9+3aFh4dr7dq1dstPnz6tH374QQ0aNCBwAwBMx5VuAADgkn7//Xd16NBBV69eVc+ePVW9enWdO3dOn376qc6fP6/58+crIiLC0WUCAFwcoRsAALispKQkzZ49Wzt27FBKSorKli2riIgI/f3vf1e9evUcXR4A4C+A0A0AAAAAgEmY0w0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASf4fR3n8N9ZF6fsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the bar plot to show distribution of star ratings given (we hope it matches the train dataset.. it does)\n",
    "plt.figure(figsize=(10, 6))  # Setting an appropriate figure size for clearer visualization\n",
    "pd.Series(stars_preds).value_counts().sort_index().plot(kind='bar', color=\"mediumseagreen\", alpha=0.85)\n",
    "\n",
    "# Adding titles, labels, and other aesthetics for clarity\n",
    "plt.title('Distribution of Predicted Star Ratings', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Star Rating', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.xticks(rotation=0)  # Ensures that the x-axis labels are horizontal\n",
    "plt.tight_layout()\n",
    "\n",
    "# Displaying the enhanced plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7147171645288389"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy\n",
    "(np.array(stars_preds).astype(int) == test_df.head(len(stars_preds))['completion'].astype(int)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8064152187874265"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# < 3 accuracy\n",
    "((np.array(stars_preds).astype(int) <= 3) == (test_df.head(len(stars_preds))['completion'].astype(int) <= 3)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8793327894745977"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy (off-by-1)\n",
    "((np.array(stars_preds).astype(int) - test_df.head(len(stars_preds))['completion'].astype(int)).abs() <= 1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## comparing against the model that was trained on 1 epoch of unshuffled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stars_preds = []\n",
    "\n",
    "# Create a tqdm progress bar\n",
    "pbar = tqdm(list(range(test_df.shape[0])), total=test_df.shape[0], desc=\"Processing rows\")\n",
    "\n",
    "# Loop through the dataframe\n",
    "for index in pbar:\n",
    "    row = test_df.iloc[index]\n",
    "    model_pred, probs = run_ft_model(row.review, 'ft:babbage-002:personal::8ELEnbVw')\n",
    "    ground_truth = str(row.star).strip()\n",
    "    stars_preds.append(model_pred)\n",
    "    if index % 1_000 == 0:\n",
    "        print(index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7148907364657282\n",
      "0.8063978615937375\n",
      "0.8721989828684499\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "print((np.array(stars_preds).astype(int) == test_df.head(len(stars_preds))['completion'].astype(int)).mean())\n",
    "\n",
    "# < 3 accuracy\n",
    "print(((np.array(stars_preds).astype(int) <= 3) == (test_df.head(len(stars_preds))['completion'].astype(int) <= 3)).mean())\n",
    "\n",
    "# Accuracy (off-by-1)\n",
    "print(((np.array(stars_preds).astype(int) - test_df.head(len(stars_preds))['completion'].astype(int)).abs() <= 1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  This model was trained on the shuffled data for a total of 4 epochs with shuffled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stars_preds = []\n",
    "\n",
    "# Create a tqdm progress bar\n",
    "pbar = tqdm(list(range(test_df.shape[0])), total=test_df.shape[0], desc=\"Processing rows\")\n",
    "\n",
    "# Loop through the dataframe\n",
    "for index in pbar:\n",
    "    row = test_df.iloc[index]\n",
    "    model_pred, probs = run_ft_model(row.review, 'ft:babbage-002:personal::8EKVvxZv')\n",
    "    ground_truth = str(row.star).strip()\n",
    "    stars_preds.append(model_pred)\n",
    "    if index % 1_000 == 0:\n",
    "        print(index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7197854650860049\n",
      "0.8088625830975648\n",
      "0.881103223230868\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "print((np.array(stars_preds).astype(int) == test_df.head(len(stars_preds))['completion'].astype(int)).mean())\n",
    "\n",
    "# < 3 accuracy\n",
    "print(((np.array(stars_preds).astype(int) <= 3) == (test_df.head(len(stars_preds))['completion'].astype(int) <= 3)).mean())\n",
    "\n",
    "# Accuracy (off-by-1)\n",
    "print(((np.array(stars_preds).astype(int) - test_df.head(len(stars_preds))['completion'].astype(int)).abs() <= 1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price to train was $39.883296\n",
      "Price for a single inference (on avg): $0.00036213104681235143 and cost to run test: $20.863456000000003\n"
     ]
    }
   ],
   "source": [
    "train_price_usd = 0.008 * job.trained_tokens / 1_000\n",
    "print(f'Price to train was ${train_price_usd}')\n",
    "\n",
    "avg_tokens_per_input = job.trained_tokens / training_df.shape[0]\n",
    "# output is always 1 token\n",
    "\n",
    "avg_inference_price_usd = 0.0120 * (avg_tokens_per_input / 1_000) + (0.0160 / 1_000)\n",
    "avg_inference_price_usd\n",
    "print(f'Price for a single inference (on avg): ${avg_inference_price_usd} and cost to run test: ${avg_inference_price_usd * test_df.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 training - no system prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0', None)"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_ft_model('I hated it', 'ft:gpt-3.5-turbo-0613:personal::8GsD6MhX', chat=True, system='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19016"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_preds = []\n",
    "\n",
    "# Create a tqdm progress bar\n",
    "pbar = tqdm(list(range(test_df.shape[0])), total=test_df.shape[0], desc=\"Processing rows\")\n",
    "\n",
    "# Loop through the dataframe\n",
    "for index in pbar:\n",
    "    if index < len(stars_preds):\n",
    "        continue\n",
    "    row = test_df.iloc[index]\n",
    "    model_pred, probs = run_ft_model(row.review, 'ft:gpt-3.5-turbo-0613:personal::8GsD6MhX', chat=True, system='')\n",
    "    ground_truth = str(row.star).strip()\n",
    "    stars_preds.append(model_pred)\n",
    "    if index % 1_000 == 0:\n",
    "        print(index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the bar plot to show distribution of star ratings given (we hope it matches the train dataset.. it does)\n",
    "plt.figure(figsize=(10, 6))  # Setting an appropriate figure size for clearer visualization\n",
    "pd.Series(stars_preds).value_counts().sort_index().plot(kind='bar', color=\"mediumseagreen\", alpha=0.85)\n",
    "\n",
    "# Adding titles, labels, and other aesthetics for clarity\n",
    "plt.title('Distribution of Predicted Star Ratings', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Star Rating', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.xticks(rotation=0)  # Ensures that the x-axis labels are horizontal\n",
    "plt.tight_layout()\n",
    "\n",
    "# Displaying the enhanced plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# note that our generative AI model is... generating predictions :) it has no forcing factor to not predict anything\n",
    "#  outside of 0, 1, 2, 3, 4 but setting temperature to a low value limits this behavior. We do have some other levers here\n",
    "#  that we aren't pulling but just calling it out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7371253015812403\n",
      "0.8197976151215871\n",
      "0.8896776769131967\n"
     ]
    }
   ],
   "source": [
    "cleaned_stars_pred = [s if s in ('0', '1', '2', '3', '4') else -1 for s in stars_preds]\n",
    "# Accuracy\n",
    "print((np.array(cleaned_stars_pred).astype(int) == test_df.head(len(cleaned_stars_pred))['completion'].astype(int)).mean())\n",
    "\n",
    "# < 3 accuracy\n",
    "print(((np.array(cleaned_stars_pred).astype(int) <= 3) == (test_df.head(len(cleaned_stars_pred))['completion'].astype(int) <= 3)).mean())\n",
    "\n",
    "# Accuracy (off-by-1)\n",
    "print(((np.array(cleaned_stars_pred).astype(int) - test_df.head(len(cleaned_stars_pred))['completion'].astype(int)).abs() <= 1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 training - with system prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0', None)"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_ft_model('I hated it', 'ft:gpt-3.5-turbo-0613:personal::8HM1JKgr', chat=True, system=system_prompt)\n",
    "# note that chat models as of this point do not offer probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stars_preds = []\n",
    "\n",
    "# Create a tqdm progress bar\n",
    "pbar = tqdm(list(range(test_df.shape[0])), total=test_df.shape[0], desc=\"Processing rows\")\n",
    "\n",
    "# Loop through the dataframe\n",
    "for index in pbar:\n",
    "    if index < len(stars_preds):\n",
    "        continue\n",
    "    row = test_df.iloc[index]\n",
    "    model_pred, probs = run_ft_model(row.review, 'ft:gpt-3.5-turbo-0613:personal::8HM1JKgr', chat=True, system=system_prompt)\n",
    "    ground_truth = str(row.star).strip()\n",
    "    stars_preds.append(model_pred)\n",
    "    if index % 1_000 == 0:\n",
    "        print(index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jk4WG7iZNmmjBggVydy/5FYZbtmwxPm8K07lzZ7sfqi5cuKDevXsrKSlJLVq0sDv7279/fyOsnzhxQk2aNNHgwYNlsViM0N24ceNSvTfatm2rDz/8UO7u7srMzFSHDh2M4f6HDx82Qnf+URAdOnTQBx98IA8PD9lsNr3++utasWJFgW3n/5x+6aWXjOMvXb22PTk5WXfccUeBIf4A/t4I3QBgkvxnr4rzOKO77rpLX331laSr4a9t27Zq1aqVmjVrpvHjx193PV26dCn1uvmHxnt7e6t///5G6C7N9a43wtdff238/cILLxiBW7p6JrFv37768MMPJV19tFRhobtr1652waR58+ZG6L58+XKJaujcubMRuKWrP7q8/PLLxnXWeWdrSxN2CpMXygpTv359jRo1qsj2P/eF1NRU44xsuXLl7ILErbfeqvbt22vx4sXKzc3V9u3b1b17d8XHxxvLtG3b1gioktSoUSO1adNGmzZtKvb+7Ny50/i7Z8+eRuCWrl6H37FjR9WpU6dYx68s7I8k1axZU2vWrNGXX36pdevWaffu3QXuM3DgwAH169dPq1atKvIyiPyaNm2qpk2bGtNZWVk6dOiQduzYoT/++MOYn/9a6fw6dep0w/qgdPVzrl+/furatWuBM9J16tSx64e5ubk6cuSIdu/ebXcZTVG1lla/fv2MffTx8VHTpk21ceNGSf/3vs7KyrL77Hruuefk4eFh7NPw4cMLDd133XWX8ff48eP173//W3fffbeaNWumf/zjHzf02AJwHYRuADBJ/jtU33rrrddcvl+/ftq+fbv+85//6NKlS1q/fr0RrPz9/dWpUycNHDhQgYGBJa7F19e30LNexVGlSpUCj+epU6eO8Xf+R1/9lRv9/O38zzYubPhp/nl/fg5ynj+HnAoVKhh/F6fea9WQ/wv6xYsXlZ6eXup/h79Srlw5+fn5KTg4WB06dFDfvn3tQmt+hfWF/M+CzsrKsqv7z44ePSrp6j0I8tSuXbvAcnXr1i1RSM3/jPIaNWrYtfn5+ZXoLuxlYX/yeHp6qmvXruratausVqv27t2rH3/8UV9//bV+++0347Xnzp37lz+U5Hf+/Hl9/vnn+ve//639+/cXega+qP7752NbEhMnTlSXLl30888/a8yYMTpw4IBsNpu+++479erVq9B1rly5opUrV2rTpk1KSEgo9NGAN/qzoTjv6/T0dOO+BpL9Z5p09akCt9xyi90PGdLVHy22bNmiNWvWyGKxaOPGjUagz/tBZ9CgQTfkWnkAroPQDQAmybvuWSr8S/yfeXt7a86cOdq6dau++uor/ec//1Fqaqqkq2fuFixYoDVr1mjFihWqVatWiWq5nsdG5f9imif/0MnCrpWWCn6RLmw716Oo182T/yZiRV2/7u3tbTdd0rNUJanhRluwYEGJrsfNU1hfyP/v6e7u/pc/DBS2T4UNpS3O6I6iXM+6UtnYn61btyo1NVVpaWl67LHHVK1aNXl5eally5Zq2bKlhg8frn/84x/GyIo/34ivKL/++quefPJJ48euJk2aqGXLlmrWrJk++ugjJSYm/uX61/sIOS8vLzVp0kSzZ8/WI488otTUVJ06dUr9+/fX2rVrVbVqVWPZc+fOqXfv3vr1118l/d+17OHh4Vq/fr2+//7766qlKMV5X//5372wflDY54abm5veeecdPf7441q3bp1++OEH47KECxcuaOXKlfriiy+0YMECu7u6A/h7I3QDgAlsNpvdHX2Le+dqNzc3NW7cWPfdd5+kq1+w4+LiNG/ePP3888+6ePGi5syZo3HjxpWonsLuwltcf/zxh06dOqWgoCBjXt4ZOkl2w7rzf7n98zDawh7Xcz2CgoKMa+UPHjxY4GzmwYMHjb9L+iNFSWoo7PXy5L/D8a233qrKlSubUkdJFNYXAgICjL+9vLz0ww8/2P1b5uTkGENvC1snL1TlV5ybB+Z32223GTfp+uWXX/Q///M/RtuFCxc0ffp03XHHHbrjjjvshvEXpizsz7/+9S/j7H2FChX01FNP2bV7eHgoMjLSCN3FHWL9/vvvG4H7pZde0nPPPWe05T0r+69cz2dBflWqVNEbb7xhXKedmpqqMWPGaMaMGcYyc+bMMY5lr1697D63vv322xtSR2lVrlxZ5cqVM34M/OWXX+yG7efdBLEo9erV0+uvv65y5crp1KlT2rNnj5YsWaK4uDhlZWVpxowZmjNnjun7AcA5cOEJAJhg1qxZxtkPLy+vIode5snJydHTTz+tFi1a6N5779W///1vSVfPkD/22GPq3LmzsWz+Ybj5g8RfnUm+3sfx5H92rtVq1dy5c43pvLtFS/bD6P9886gNGzYUuf38+1HcM4r5n9P74YcfGqMCpKtfoBctWmRMd+jQoVjbLKn8NXz55ZeKi4szpi9duqR3333XmG7fvv0NfSxSaRVWQ1BQkPHYsMzMTLvHLV25ckUdOnTQ/fffr+eff16HDh2SdPX69zz/+c9/7B4pt2fPHm3btq1EdbVo0cL4e8WKFXaXZ6xevVoLFy7UG2+8oQkTJhjzi+o3ZWF/8n44k66+f/L/UCVdHd68fPlyY7pBgwbG3/l/EPjz+zp/Xfkfd5aUlGTXVtSQ7RvZBzt06KCOHTsa0/mHWv9VrWfOnLG7hj9/rcX9TLte5cqVU2hoqDH98ccfGyMccnNzNWXKlELXGzlypO655x61atVKn3/+uaSr/a1Lly7GUwgk+89pAOBMNwBchwsXLhhfzmw2my5fvqx9+/bpwIEDxjIvvvii3dngwnh4eKhWrVrG2fFhw4apXbt2uu2223Tq1CnjDtmSfcjNP1Q0MTFRY8aMka+vr6Kjo2/I/uVZuXKljh07piZNmmjnzp3GHaPd3NzsvmjmDw4//PCD3njjDd11113atGnTX971OP9+zJkzR/v379f//M//qH379kWu07t3b3322Wc6d+6cTp48qUceeURt27ZVVlaWNm3aZFw72qRJEz388MOl3ve/0rJlS0VGRuqnn35Sdna2+vfvr3bt2hmPDMu7WVTFihU1ZMgQU2q4UZ5++mnjhn3jxo3T5s2bVbt2bf3444/GD0g5OTnGta/NmzdXw4YNlZSUpKysLPXp00edOnWS1WrVhg0bSjy0vmfPnpozZ47++OMP/fzzz3r44YfVpk0bpaen2wW5vn37Gn/n7zcrV67UxYsX1bBhQ/Xq1cvh+/Pss89q7dq1yszM1Pnz59W5c2e1bdtWgYGBOnfunLZs2WJcL+zh4aH+/fsXul9nz541nhzwzjvvyN/f37h0ZcKECdq3b58sFou+/fZbu5B6o29OVpTXX39dP/74o/EjyYQJE9S6dWv5+PjYfe7Nnj1bycnJ8vHx0YYNG4q86Vv+fd+8ebPGjh2rgIAAPf/88ze89ieffNJ4zNrGjRv1+OOPKzQ0VPHx8YWOXJGu3qTwiy++kCS99dZb+uGHH1SrVi2lpaXZDZfP/zkNAJzpBoDrcOnSJX3yySf65JNPNHv2bC1dutQucPfu3bvYXxZfeeUVY9is1WrVN998o3nz5tl9mY6IiDDuhi1JoaGhxnDR7OxsLVu27IYP22zdurXq1aunvXv3auHChXaPpRo6dKjdkMyOHTsaZxilq48TGzNmjLZs2aI+ffoU+Rr5v6D++uuvWrZsWZFfevNUr15d06dPV7Vq1SRdvX501apVWrdunRG469evr5kzZxYYSnyjuLu767333jNuopaVlaUNGzZo2bJlRuC+9dZbNXPmzOu6gdXN0KdPH7u7mv/www9auHChcaOxW265RdOmTZOXl5ekqz+4TJkyxTiDefnyZa1YsUJffPGFcnJy9OSTT5bo9atWraqpU6caN4A7deqUli1bpg0bNhhnILt06aJHH33UWCd/v0lNTdWSJUuMa6MdvT/BwcF6//33Vb58eUlX+8Z3332nBQsWaP369UboLFeunMaOHWt3t/S6devanRles2aNvvjiC2VnZ2vQoEHG2erMzEytWLFC69atk8VisXtEXGFD5M0QEBCgl156yZhOTk42nhrQt29f4/jm5ORo/fr1WrFihf74448ia83/b3r58mUtWbKkxKMMiuuhhx5S165djekDBw5oyZIlOnjwoKKiouxG7uSdgR84cKA6depk7NPmzZs1b948rV+/3vjcqVu3rv73f//XlJoBOCdCNwDcQL6+vgoODtYjjzyixYsXKyYmptjDOcuXL69PP/1UEyZMUPPmzRUQECBPT0/dcsstCg8P1+jRozV//ny7mwT5+/vr3Xff1Z133qly5cqpcuXKhd5F+3pUrFhRy5YtU79+/eTv7y9vb281adJE7733nt1zd/P2YcmSJXrkkUdUuXJl+fr6KjQ0VFOnTtVrr71W5Gs89dRT6tu3r6pUqSIvLy/dfvvtdtdLFyUyMlLr1q3T0KFD1bBhQ5UvX17ly5dX48aN9corr2j58uV21+qaoXr16lq2bJnGjh2rFi1aqFKlSvLy8lKtWrXUv39/rVu3zm7oclnl7u6uKVOmaPLkyWrVqpXdfvTq1UurV6+2+4FFku68806tXLlSXbp0UaVKlVS+fHndc889Wrx4sdq2bVviGu677z6tXr1a3bt3V2BgoHFX9oiICE2cOLHAkN+OHTtq2LBhCggIULly5RQYGGicuS4L+xMVFaWvv/5azz33nBo3bqxbbrlFHh4euvXWW1W/fn3169dPX3zxhd0jzaSrl6TExsYqJCRE5cqV06233qqWLVsqMzNTbdu21aeffqrmzZurYsWK8vHxUcOGDTV69Gi9//77xjZu5jXTvXv3VlhYmDE9d+5c/frrr7rrrru0bNkytWnTRpUrV5aXl5fq1q2rYcOG2Q35/+6774yRBE2aNFFMTIyCg4NVrlw5+fv7242gudHefvttjRo1SvXq1ZOXl5dq1KihYcOGafr06XbL5f0Y5OHhoXfffVfTp09X69atVaNGDZUrV04VKlRQ48aNjUeNmfGUAgDOy81m5q1VAQAAgDJozZo1unDhgipVqqQaNWrY3aDvxIkT6tixo3Jzc1WhQgXFxcWViXsyAHBOXNMNAACAv52dO3dq1apVkq6OjHjggQcUGBio9PR0bdmyxbjBW9u2bQncAK4LZ7oBAADwt3P06FE98cQTf/losCpVqmjZsmW6/fbbb2JlAFwNoRsAAAB/SydPntSnn36qXbt26eTJk8rMzJSPj49q1qyp1q1b6+mnn77m0ycA4FoI3QAAAAAAmIS7lwMAAAAAYBJCNwAAAAAAJuHu5TdAbm6usrOz5e7uzt0tAQAAAOBvwGazKTc3V56ennJ3L/p8NqH7BsjOzlZiYqKjywAAAAAA3GQhISHy8vIqsp3QfQPk/aoREhIiDw8PB1fjunJycpSYmMhxhtOjL8OV0J/hSujPcBX05Zsj7zj/1VluidB9Q+QNKffw8KBT3wQcZ7gK+jJcCf0ZroT+DFdBX745rnWJMTdSAwAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAABfj4+Pj6BLw/3k6ugAAAAAAKMsys63Kys12dBklElgnWFdyLFKOoyspvnLunvLx9HJ0GTccoRsAAAAA/kJWbrY+TvpSKRnpji6l2KxWi7y8vB1dRrEF+FbScw07y0eEbgAAAAD420nJSFfylTRHl1FsmZmZDDEvI7imGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJOU2dA9ePBgvfbaa8b0wYMH9fjjjys0NFSPPfaY9u/fb7f8+vXr1b59e4WGhmrIkCE6d+6c0Waz2TRlyhS1atVKLVq00KRJk5Sbm2u0nz9/XsOGDVN4eLjatWuntWvXmr+DAAAAAACXVyZD95dffqktW7YY01euXNHgwYMVGRmpVatWKTw8XM8995yuXLkiSdq3b59Gjx6toUOHatmyZbp48aKio6ON9T/99FOtX79esbGxmjZtmtatW6dPP/3UaI+OjtYff/yhZcuW6YUXXtDrr7+uffv23bwdBgAAAAC4pDIXutPT0zVp0iSFhIQY87766it5e3vrlVdeUb169TR69GhVqFBB33zzjSRp0aJF6tSpk7p166aGDRtq0qRJ2rJli06cOCFJWrBggYYPH67IyEi1atVKI0aM0OLFiyVJx48f1+bNm/Xmm2+qfv36evzxx/XII49oyZIlN3/nAQAAAAAupcyF7nfeeUddu3bVHXfcYcxLSEhQRESE3NzcJElubm5q1qyZ4uPjjfbIyEhj+cDAQAUFBSkhIUEpKSk6ffq0mjdvbrRHREQoOTlZZ8+eVUJCggIDA1WzZk279r1795q8pwAAAAAAV+fp6ALy2759u3766SetW7dOMTExxvzU1FS7EC5JVatW1eHDhyVJZ8+eVfXq1Qu0nzlzRqmpqZJk116tWjVJMtoLWzclJaXE9efk5JR4HRRf3vHlOMPZ0ZfhSujPcCX0Z7gKm81m/D/vxKWzcKb3X3FrLTOh22Kx6I033tCYMWPk4+Nj15aRkSEvLy+7eV5eXrJarZKkzMzMItszMzON6fxtkmS1Wq+57ZJITEws8TooOY4zXAV9Ga6E/gxXQn9Gfj4+PgqsEyyr1WJkC2dhsVgcXUKxWT0tysrOVtLhX5zuOF9LmQndsbGxatKkidq0aVOgzdvbu0AItlqtRjgvqt3X19cuYHt7ext/S5Kvr+81t10SISEh8vDwKPF6KJ6cnBwlJiZynOH06MtwJfRnuBL6M4pyJcciLy/vUmUER7DZbLJYLPL29naaM91eXt4q5+mphg0bOrqUYsv7zLiWMhO6v/zyS6WlpSk8PFzS/wXjDRs2qEuXLkpLS7NbPi0tzRgWHhAQUGi7v7+/AgICJF0dop533XbekPO89qLWLSkPDw8+oG8CjjNcBX0ZroT+DFdCf0YBzjPiWZLs7oXlbFzxvVdmbqS2cOFCrVu3TmvWrNGaNWvUrl07tWvXTmvWrFFoaKj27t1rd23Cnj17FBoaKkkKDQ1VXFycsa3Tp0/r9OnTCg0NVUBAgIKCguza4+LiFBQUpOrVqyssLEzJyck6c+aMXXtYWNjN2XEAAAAAgMsqM2e6a9SoYTddoUIFSVKtWrVUtWpVTZ06VW+99ZaeeOIJffbZZ8rIyFCnTp0kSb1791bfvn0VFhamkJAQvfXWW2rbtq2Cg4ON9ilTpui2226TJE2dOlUDBw6UJAUHB6t169YaOXKkRo8ercTERK1fv16LFi26WbsOAAAAAHBRZSZ0/xU/Pz99/PHHeuONN/T555+rQYMGmjVrlsqXLy9JCg8P17hx4zRt2jRduHBB9957r8aPH2+sP2jQIP3+++8aOnSoPDw81KNHDw0YMMBonzRpkkaPHq2ePXvK399fEyZMUNOmTW/2bgIAAAAAXEyZDd1vv/223XTTpk21evXqIpfv3r27unfvXmibh4eHoqOjFR0dXWh71apV9dFHH5W+WAAAAAAAClFmrukGAAAAAMDVELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJOUqdD922+/adCgQQoPD1fbtm01e/Zso+3NN99UgwYN7P5btGiR0b5+/Xq1b99eoaGhGjJkiM6dO2e02Ww2TZkyRa1atVKLFi00adIk5ebmGu3nz5/XsGHDFB4ernbt2mnt2rU3Z4cBAAAAAC7N09EF5MnNzdXgwYMVEhKi1atX67ffftNLL72kgIAAPfzwwzp69KhefvllPfroo8Y6fn5+kqR9+/Zp9OjRGjt2rBo2bKi33npL0dHR+vjjjyVJn376qdavX6/Y2FhlZ2dr5MiRqlq1qgYNGiRJio6OVmZmppYtW6aEhAS9/vrrqlOnjpo2bXrzDwQAAAAAwGWUmdCdlpamRo0aKSYmRn5+fqpdu7buvvtuxcXFGaF70KBB8vf3L7DuokWL1KlTJ3Xr1k2SNGnSJEVFRenEiRMKDg7WggULNHz4cEVGRkqSRowYoQ8++ECDBg3S8ePHtXnzZm3atEk1a9ZU/fr1FR8fryVLlhC6AQAAAADXpcwML69evbref/99+fn5yWazKS4uTrt371aLFi106dIlpaSkqHbt2oWum5CQYARqSQoMDFRQUJASEhKUkpKi06dPq3nz5kZ7RESEkpOTdfbsWSUkJCgwMFA1a9a0a9+7d69p+woAAAAA+HsoM2e682vXrp1OnTqlqKgoPfDAA9q/f7/c3Nz00UcfaevWrapUqZKefvppY6j52bNnVb16dbttVK1aVWfOnFFqaqok2bVXq1ZNkoz2wtZNSUkpcd05OTklXgfFl3d8Oc5wdvRluBL6M1wJ/RmuwmazGf93c3NzcDUl40zvv+LWWiZD97Rp05SWlqaYmBhNnDhRjRs3lpubm+rWraunnnpKu3fv1r/+9S/5+fmpQ4cOyszMlJeXl902vLy8ZLValZmZaUznb5Mkq9WqjIyMItctqcTExBKvg5LjOMNV0JfhSujPcCX0Z+Tn4+OjwDrBslotRrZwFhaLxdElFJvV06Ks7GwlHf7F6Y7ztZTJ0B0SEiLpaicZMWKE9uzZo6ioKFWqVEmS1LBhQ/36669aunSpOnToIG9v7wIh2Wq1ytfX1y5ge3t7G39Lkq+vb5Hr+vj4lKpuDw+PEq+H4snJyVFiYiLHGU6PvgxXQn+GK6E/oyhXcizy8vIuVUZwBJvNJovFIm9vb6c50+3l5a1ynp5q2LCho0sptrzPjGspM6E7LS1N8fHxat++vTHvjjvuUFZWli5duqQqVarYLV+3bl3t2LFDkhQQEKC0tLQC2/P391dAQIAkKTU11bhuO2/IeV57UeuWlIeHBx/QNwHHGa6CvgxXQn+GK6E/owDnGfEsSUbQdpbAnZ8rvvfKzI3UTp48qaFDh9pdS71//35VqVJFCxcu1IABA+yWT0pKUt26dSVJoaGhiouLM9pOnz6t06dPKzQ0VAEBAQoKCrJrj4uLU1BQkKpXr66wsDAlJyfrzJkzdu1hYWHm7CgAAAAA4G+jzITukJAQNW7cWKNGjdKRI0e0ZcsWTZ48Wc8//7yioqK0e/duzZkzR8ePH9eSJUu0Zs0aDRw4UJLUu3dvrV27VsuXL1dSUpJeeeUVtW3bVsHBwUb7lClTtHPnTu3cuVNTp05Vv379JEnBwcFq3bq1Ro4cqaSkJC1fvlzr16/Xk08+6bBjAQAAAABwDWVmeLmHh4dmzpyp8ePHq1evXvL19VXfvn3Vr18/ubm56YMPPtC0adP0wQcfqEaNGpo6darCw8MlSeHh4Ro3bpymTZumCxcu6N5779X48eONbQ8aNEi///67hg4dKg8PD/Xo0cPuzPmkSZM0evRo9ezZU/7+/powYQLP6AYAAAAAXLcyE7qlq9dmx8bGFtrWvn17u+u9/6x79+7q3r17oW0eHh6Kjo5WdHR0oe1Vq1bVRx99VPKCAQAAAAD4C2VmeDkAAAAAAK6G0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmKRMhe7ffvtNgwYNUnh4uNq2bavZs2cbbSdOnNCAAQMUFhamhx56SNu2bbNb98cff1SXLl0UGhqqfv366cSJE3bt8+bNU5s2bRQeHq5Ro0YpIyPDaLNYLBo1apQiIyPVunVrzZ0719wdBQAAAAD8LZSZ0J2bm6vBgwercuXKWr16tcaOHasPP/xQ69atk81m05AhQ1StWjWtXLlSXbt21dChQ3Xq1ClJ0qlTpzRkyBB1795dK1asUJUqVfTiiy/KZrNJkjZs2KDY2FiNGzdO8+fPV0JCgiZPnmy89qRJk7R//37Nnz9fb7zxhmJjY/XNN9845DgAAAAAAFyHp6MLyJOWlqZGjRopJiZGfn5+ql27tu6++27FxcWpWrVqOnHihD777DOVL19e9erV0/bt27Vy5UoNGzZMy5cvV5MmTTRw4EBJ0sSJE3Xvvfdq165datmypRYsWKD+/fsrKipKkjR27FgNGjRII0eOlM1m0/Lly/XJJ5+ocePGaty4sQ4fPqzFixfrwQcfdOQhAQAAAAA4uTJzprt69ep6//335efnJ5vNpri4OO3evVstWrRQQkKC7rrrLpUvX95YPiIiQvHx8ZKkhIQERUZGGm2+vr5q3Lix4uPjlZOTo8TERLv2sLAwZWVlKSkpSUlJScrOzlZ4eLjdthMSEpSbm2v+jgMAAAAAXFaZOdOdX7t27XTq1ClFRUXpgQce0IQJE1S9enW7ZapWraozZ85IklJTU4tsv3jxoiwWi127p6enKlWqpDNnzsjd3V2VK1eWl5eX0V6tWjVZLBalp6erSpUqxa47JyenNLuLYso7vhxnODv6MlwJ/RmuhP4MV5F3ma3NZpObm5uDqykZZ3r/FbfWMhm6p02bprS0NMXExGjixInKyMiwC8WS5OXlJavVKkl/2Z6ZmWlMF9Zus9kKbZNkbL+4EhMTS7Q8SofjDFdBX4YroT/DldCfkZ+Pj48C6wTLarUY2cJZWCwWR5dQbFZPi7Kys5V0+BenO87XUiZDd0hIiKSrnWTEiBF67LHH7O42Ll0NxD4+PpIkb2/vAgHZarWqYsWK8vb2Nqb/3O7r66ucnJxC2yQZ2y9J3R4eHiVaB8WXd6kAxxnOjr4MV0J/hiuhP6MoV3Is8vLyLnE+cBSbzSaLxSJvb2+nOdPt5eWtcp6eatiwoaNLKba8z4xrKTOhOy0tTfHx8Wrfvr0x74477lBWVpb8/f117NixAsvnDRkPCAhQWlpagfZGjRqpUqVK8vb2VlpamurVqydJys7OVnp6uvz9/WWz2XT+/HllZ2fL0/Pq4UhNTZWPj48qVqxYon3w8PDgA/om4DjDVdCX4Uroz3Al9GcU4DwjniXJCNrOErjzc8X3Xpm5kdrJkyc1dOhQpaSkGPP279+vKlWqKCIiQgcOHLAbZhAXF6fQ0FBJUmhoqOLi4oy2jIwMHTx4UKGhoXJ3d1dISIhde3x8vDz//68ojRo1kqenp3FTtrxth4SEyN29zBweAAAAAIATKjOpMiQkRI0bN9aoUaN05MgRbdmyRZMnT9bzzz+vFi1aKDAwUNHR0Tp8+LBmzZqlffv2qUePHpKkxx57THv27NGsWbN0+PBhRUdHq2bNmmrZsqUkqU+fPpozZ442btyoffv2KSYmRj179pSvr698fX3VrVs3xcTEaN++fdq4caPmzp2rfv36OfJwAAAAAABcQJkZXu7h4aGZM2dq/Pjx6tWrl3x9fdW3b1/169dPbm5umjlzpkaPHq3u3burVq1amjFjhoKCgiRJNWvW1PTp0zVhwgTNmDFD4eHhmjFjhjGconPnzkpOTtaYMWNktVrVsWNHjRw50njt6OhoxcTEqH///vLz89OwYcPUsWNHhxwHAAAAAIDrKDOhW7p6bXZsbGyhbbVq1dKiRYuKXPe+++7TfffdV2T74MGDNXjw4ELbfH199c477+idd94pWcEAAAAAAPyFMjO8HAAAAAAAV0PoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMUqZCd0pKioYPH64WLVqoTZs2mjhxoiwWiyTpzTffVIMGDez+W7RokbHu+vXr1b59e4WGhmrIkCE6d+6c0Waz2TRlyhS1atVKLVq00KRJk5Sbm2u0nz9/XsOGDVN4eLjatWuntWvX3rydBgAAAAC4LE9HF5DHZrNp+PDhqlixohYvXqwLFy5o1KhRcnd316uvvqqjR4/q5Zdf1qOPPmqs4+fnJ0nat2+fRo8erbFjx6phw4Z66623FB0drY8//liS9Omnn2r9+vWKjY1Vdna2Ro4cqapVq2rQoEGSpOjoaGVmZmrZsmVKSEjQ66+/rjp16qhp06Y3/0AAAAAAAFxGmQndx44dU3x8vP7zn/+oWrVqkqThw4frnXfeMUL3oEGD5O/vX2DdRYsWqVOnTurWrZskadKkSYqKitKJEycUHBysBQsWaPjw4YqMjJQkjRgxQh988IEGDRqk48ePa/Pmzdq0aZNq1qyp+vXrKz4+XkuWLCF0AwAAAACuS5kZXu7v76/Zs2cbgTvPpUuXdOnSJaWkpKh27dqFrpuQkGAEakkKDAxUUFCQEhISlJKSotOnT6t58+ZGe0REhJKTk3X27FklJCQoMDBQNWvWtGvfu3fvjd1BAAAAAMDfTpk5012xYkW1adPGmM7NzdWiRYvUqlUrHT16VG5ubvroo4+0detWVapUSU8//bQx1Pzs2bOqXr263faqVq2qM2fOKDU1VZLs2vOCfV57YeumpKSUeB9ycnJKvA6KL+/4cpzh7OjLcCX0Z7gS+jNchc1mM/7v5ubm4GpKxpnef8WttcyE7j+bPHmyDh48qBUrVujAgQNyc3NT3bp19dRTT2n37t3617/+JT8/P3Xo0EGZmZny8vKyW9/Ly0tWq1WZmZnGdP42SbJarcrIyChy3ZJKTEws8TooOY4zXAV9Ga6E/gxXQn9Gfj4+PgqsEyyr1WJkC2eRd1NqZ2D1tCgrO1tJh39xuuN8LWUydE+ePFnz58/Xe++9p/r16+vOO+9UVFSUKlWqJElq2LChfv31Vy1dulQdOnSQt7d3gZBstVrl6+trF7C9vb2NvyXJ19e3yHV9fHxKXHdISIg8PDxKvB6KJycnR4mJiRxnOD36MlwJ/RmuhP6MolzJscjLy7tUGcERbDabLBaLvL29neZMt5eXt8p5eqphw4aOLqXY8j4zrqXMhe7x48dr6dKlmjx5sh544AFJkpubmxG489StW1c7duyQJAUEBCgtLc2uPS0tTf7+/goICJAkpaamGtdt5w05z2svat2S8vDw4AP6JuA4w1XQl+FK6M9wJfRnFOA8I54lyQjazhK483PF916ZuZGaJMXGxuqzzz7Tu+++q86dOxvzP/jgAw0YMMBu2aSkJNWtW1eSFBoaqri4OKPt9OnTOn36tEJDQxUQEKCgoCC79ri4OAUFBal69eoKCwtTcnKyzpw5Y9ceFhZmzk4CAAAAAP42ysyZ7qNHj2rmzJkaPHiwIiIijLPRkhQVFaVZs2Zpzpw56tChg7Zt26Y1a9ZowYIFkqTevXurb9++CgsLU0hIiN566y21bdtWwcHBRvuUKVN02223SZKmTp2qgQMHSpKCg4PVunVrjRw5UqNHj1ZiYqLWr1+vRYsW3eQjAAAAAABwNWUmdG/atEk5OTn68MMP9eGHH9q1HTp0SB988IGmTZumDz74QDVq1NDUqVMVHh4uSQoPD9e4ceM0bdo0XbhwQffee6/Gjx9vrD9o0CD9/vvvGjp0qDw8PNSjRw+7M+eTJk3S6NGj1bNnT/n7+2vChAk8oxsAAAAAcN3cbHn3k0ep5eTkKD4+XmFhYS55DUJZwXGGq6Avw5XQn+FK6M8oyh/WKxq3d7GSr6Rde+EyIjMz02lu/CZJNcpX05jwJ3WLV3lHl1Jsxf3MKFPXdAMAAAAA4EoI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJSh26+/Xrp+3btxfZvnHjRj344IOl3TwAAAAAAE6v2M/pzsjI0Pnz543pXbt2qUOHDqpVq1aBZXNzc7Vt2zadOnXqxlQJAAAAAIATKnbo/uOPP9S5c2dlZmYa8yZMmKAJEyYUuU5YWNh1FQcAAAAAgDMrduiuXr26Ro8erZ07d0qS1q1bp7CwMNWsWbPAsh4eHqpataqeeuqpG1cpAAAAAABOptihW5J69OihHj16SJLi4uL0zDPP6P777zelMAAAAAAAnF2JQnd+33///Y2sAwAAAAAAl1Pq0C1JNptNO3bsUGpqqnJzcwtdplu3btfzEgAAAAAAOK1Sh+5ffvlFzzzzTJF3KLfZbHJzcyN0AwAAAAD+tkoduqdOnapTp06pW7duatKkiby8vG5kXQAAAAAAOL1Sh+4dO3aod+/eGjNmzI2sBwAAAAAAl+Fe2hVzcnLUqFGjG1kLAAAAAAAupdShu0mTJjpw4MCNrAUAAAAAAJdS6tD9j3/8Q2vXrtVXX32lnJycG1kTAAAAAAAuodTXdH/88ceqWLGiXn75ZUVHR+vWW2+Vu7t9hndzc9PmzZuvu0gAAAAAAJxRqUP30aNH5eHhocDAwBtZDwAAAAAALqPUofv777+/kXUAAAAAAOBySn1NNwAAAAAA+GulPtMdGxt7zWXc3Nw0ZMiQ0r4EAAAAAABOzZTQ7ebmJpvNRugGAAAAAPytlTp0f/rppwXmZWdnKy0tTV988YVSU1P14YcfXldxAAAAAAA4s1KH7rvvvrvItm7duumpp57S/Pnz9frrr5f2JQAAAAAAcGqm3EjNzc1NnTt31jfffGPG5gEAAAAAcAqm3b08IyNDf/zxh1mbBwAAAACgzCv18PLc3NxC51utVu3fv1/z5s1TvXr1Sl0YAAAAAADOrtSh+6677pKbm1uR7TabTa+99lppNw8AAAAAgNMrdehu3rx54Rv09FT16tXVrVu3v7zZGgAAAAAArq7UoXvhwoU3sg4AAAAAAFxOqUN3fufOndPJkyfl5eWl2267TZUqVboRmwUAAAAAwKldV+j+7bffNGbMGO3atcuY5+bmppYtW2rMmDGqU6fOdRcIAAAAAICzKnXoTklJUe/evXXu3Dk1b95c9evXV05Ojg4fPqzt27erT58+Wrt2rapXr34j6wUAAAAAwGmUOnTHxsbq4sWLmj17tlq3bm3Xtm3bNj3//PP6+OOP9a9//eu6iwQAAAAAwBm5l3bFrVu3qmfPngUCtyS1bt1aPXv21ObNm6+rOAAAAAAAnFmpQ/fvv/+uBg0aFNlev359paamlnbzAAAAAAA4vVKH7qpVq+rw4cNFth85ckSVK1cu7eYBAAAAAHB6pQ7dbdq00eeff2535/I827dv1+eff17o0HMAAAAAAP4uSn0jtWHDhmnjxo3q37+/WrZsqTvuuEOSdPjwYe3atUsVK1bU0KFDb1ihAAAAAAA4m1KH7oCAAC1ZskRjx47Vzp07tWPHDqOtefPmiomJUVBQ0A0pEgAAAAAAZ1Tq0C1JdevW1fz583X+/HmdOHFCklSzZk1VqVLlhhQHAAAAAIAzu67Qnady5cqqXLmyzpw5Iz8/vxuxSQAAAAAAnF6Jb6S2ZMkSde7cWTk5OQXaJk+erDZt2mjBggWlKiYlJUXDhw9XixYt1KZNG02cOFEWi0WSdOLECQ0YMEBhYWF66KGHtG3bNrt1f/zxR3Xp0kWhoaHq16+fceY9z7x589SmTRuFh4dr1KhRysjIMNosFotGjRqlyMhItW7dWnPnzi1V/QAAAAAA5Fei0P32229r3Lhx+u233/Tbb78VaK9QoYKuXLmiiRMnauzYsSUqxGazafjw4crIyNDixYv13nvvafPmzXr//fdls9k0ZMgQVatWTStXrlTXrl01dOhQnTp1SpJ06tQpDRkyRN27d9eKFStUpUoVvfjii7LZbJKkDRs2KDY2VuPGjdP8+fOVkJCgyZMnG689adIk7d+/X/Pnz9cbb7yh2NhYffPNNyWqHwAAAACAPyt26N60aZPmzZunu+++W999953q1q1bYJlx48Zp06ZNCg0N1WeffaYffvih2IUcO3ZM8fHxmjhxou68805FRkZq+PDhWr9+vXbs2KETJ05o3Lhxqlevnp577jmFhYVp5cqVkqTly5erSZMmGjhwoO68805NnDhRycnJxuPMFixYoP79+ysqKkpNmzbV2LFjtXLlSmVkZOjKlStavny5Ro8ercaNG6tDhw565plntHjx4mLXDgAAAABAYYodupcuXarg4GDNmjVLgYGBRS5XvXp1zZ49W1WqVClRcPX399fs2bNVrVo1u/mXLl1SQkKC7rrrLpUvX96YHxERofj4eElSQkKCIiMjjTZfX181btxY8fHxysnJUWJiol17WFiYsrKylJSUpKSkJGVnZys8PNxu2wkJCcrNzS12/QAAAAAA/FmxQ/f+/fvVtWtXlStX7prL+vn5qWvXrkpISCh2IRUrVlSbNm2M6dzcXC1atEitWrVSamqqqlevbrd81apVdebMGUn6y/aLFy/KYrHYtXt6eqpSpUo6c+aMUlNTVblyZXl5eRnt1apVk8ViUXp6erHrBwAAAADgz4p99/LLly/rtttuK/aG69Spo0uXLpWqKOnqTdkOHjyoFStWaN68eXahWJK8vLxktVolSRkZGUW2Z2ZmGtOFtdtstkLbJBnbL67Cbi6HGyfv+HKc4ezoy3Al9Ge4EvozXEXeva1sNpvc3NwcXE3JONP7r7i1Fjt0+/v76+zZs8Uu4Ny5cwWGihfX5MmTNX/+fL333nuqX7++vL29C5x1tlqt8vHxkSR5e3sXCMhWq1UVK1aUt7e3Mf3ndl9fX+Xk5BTaJsnYfnElJiaWaHmUDscZroK+DFdCf4YroT8jPx8fHwXWCZbVajFO6DmLvCdBOQOrp0VZ2dlKOvyL0x3nayl26G7SpIk2btyoF198sVjLb9iwQbVr1y5xQePHj9fSpUs1efJkPfDAA5KkgIAAHTlyxG65tLQ0Y8h4QECA0tLSCrQ3atRIlSpVkre3t9LS0lSvXj1JUnZ2ttLT0+Xv7y+bzabz588rOztbnp5XD0dqaqp8fHxUsWLFEtUeEhIiDw+PEu8ziifv+nyOM5wdfRmuhP4MV0J/RlGu5Fjk5eVd4pNyjmKz2WSxWOTt7e00Z7q9vLxVztNTDRs2dHQpxZb3mXEtxQ7djz76qF544QXNnj1bzzzzzF8uO3v2bP33v//V22+/XdzNS5JiY2P12Wef6d1339WDDz5ozA8NDdWsWbOUmZlpdPS4uDhFREQY7XFxccbyGRkZOnjwoIYOHSp3d3eFhIQoLi5OLVu2lCTFx8fLM98/qKenp+Lj442brcXFxSkkJETu7iV7jLmHhwcf0DcBxxmugr4MV0J/hiuhP6MA5xnxLElG0HaWwJ2fK773ip0qo6Ki1LFjR02dOlX/+Mc/9NNPPykrK8toz8rK0q5duzR8+HBNnTpVzZs3V+fOnYtdyNGjRzVz5kw9++yzioiIUGpqqvFfixYtFBgYqOjoaB0+fFizZs3Svn371KNHD0nSY489pj179mjWrFk6fPiwoqOjVbNmTSNk9+nTR3PmzNHGjRu1b98+xcTEqGfPnvL19ZWvr6+6deummJgY7du3Txs3btTcuXPVr1+/YtcOAAAAAEBhin2mW5ImTpwoNzc3bdiwQd9++608PDxUqVIl5ebm6sKFC8rNzZXNZlOHDh00YcIEY7h2cWzatEk5OTn68MMP9eGHH9q1HTp0SDNnztTo0aPVvXt31apVSzNmzFBQUJAkqWbNmpo+fbomTJigGTNmKDw8XDNmzDB+2encubOSk5M1ZswYWa1WdezYUSNHjjS2Hx0drZiYGPXv319+fn4aNmyYOnbsWJJDAwAAAABAAW62vFvblcC2bdu0Zs0aJSYm6uzZs/Lw8FBAQICaN2+uLl262D0T++8gJydH8fHxCgsLc8nhEGUFxxmugr4MV0J/hiuhP6Mof1ivaNzexUq+knbthcuI/JfmOoMa5atpTPiTusWrvKNLKbbifmaU6Ex3ntatW6t169alLg4AAAAAgL+Dkt0pDAAAAAAAFBuhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAk5TJ0G21WtWlSxft3LnTmPfmm2+qQYMGdv8tWrTIaF+/fr3at2+v0NBQDRkyROfOnTPabDabpkyZolatWqlFixaaNGmScnNzjfbz589r2LBhCg8PV7t27bR27dqbs6MAAAAAAJfm6egC/sxisejll1/W4cOH7eYfPXpUL7/8sh599FFjnp+fnyRp3759Gj16tMaOHauGDRvqrbfeUnR0tD7++GNJ0qeffqr169crNjZW2dnZGjlypKpWrapBgwZJkqKjo5WZmally5YpISFBr7/+uurUqaOmTZvepL0GAAAAALiiMhW6jxw5opdfflk2m61A29GjRzVo0CD5+/sXaFu0aJE6deqkbt26SZImTZqkqKgonThxQsHBwVqwYIGGDx+uyMhISdKIESP0wQcfaNCgQTp+/Lg2b96sTZs2qWbNmqpfv77i4+O1ZMkSQjcAAAAA4LqUqeHlu3btUsuWLbVs2TK7+ZcuXVJKSopq165d6HoJCQlGoJakwMBABQUFKSEhQSkpKTp9+rSaN29utEdERCg5OVlnz55VQkKCAgMDVbNmTbv2vXv33tidAwAAAAD87ZSpM919+vQpdP7Ro0fl5uamjz76SFu3blWlSpX09NNPG0PNz549q+rVq9utU7VqVZ05c0apqamSZNderVo1STLaC1s3JSXlhu0XAAAAAODvqUyF7qIcO3ZMbm5uqlu3rp566int3r1b//rXv+Tn56cOHTooMzNTXl5edut4eXnJarUqMzPTmM7fJl29YVtGRkaR65ZUTk5OiddB8eUdX44znB19Ga6E/gxXQn+Gq8i7XNdms8nNzc3B1ZSMM73/ilurU4Tubt26KSoqSpUqVZIkNWzYUL/++quWLl2qDh06yNvbu0BItlqt8vX1tQvY3t7ext+S5OvrW+S6Pj4+Ja4zMTGxxOug5DjOcBX0ZbgS+jNcCf0Z+fn4+CiwTrCsVotxQs9ZWCwWR5dQbFZPi7Kys5V0+BenO87X4hSh283NzQjceerWrasdO3ZIkgICApSWlmbXnpaWJn9/fwUEBEiSUlNTjeu284ac57UXtW5JhYSEyMPDo8TroXhycnKUmJjIcYbToy/DldCf4UrozyjKlRyLvLy8S3VizhFsNpssFou8vb2d5ky3l5e3ynl6qmHDho4updjyPjOuxSlC9wcffKC9e/dq3rx5xrykpCTVrVtXkhQaGqq4uDh1795dknT69GmdPn1aoaGhCggIUFBQkOLi4ozQHRcXp6CgIFWvXl1hYWFKTk7WmTNndNtttxntYWFhJa7Tw8ODD+ibgOMMV0FfhiuhP8OV0J9RgPOMeJYkI2g7S+DOzxXfe2Xq7uVFiYqK0u7duzVnzhwdP35cS5Ys0Zo1azRw4EBJUu/evbV27VotX75cSUlJeuWVV9S2bVsFBwcb7VOmTNHOnTu1c+dOTZ06Vf369ZMkBQcHq3Xr1ho5cqSSkpK0fPlyrV+/Xk8++aTD9hcAAAAA4Bqc4kx306ZN9cEHH2jatGn64IMPVKNGDU2dOlXh4eGSpPDwcI0bN07Tpk3ThQsXdO+992r8+PHG+oMGDdLvv/+uoUOHysPDQz169NCAAQOM9kmTJmn06NHq2bOn/P39NWHCBJ7RDQAAAAC4bmU2dB86dMhuun379mrfvn2Ry3fv3t0YXv5nHh4eio6OVnR0dKHtVatW1UcffVT6YgEAAAAAKIRTDC8HAAAAAMAZEboBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJOUydBttVrVpUsX7dy505h34sQJDRgwQGFhYXrooYe0bds2u3V+/PFHdenSRaGhoerXr59OnDhh1z5v3jy1adNG4eHhGjVqlDIyMow2i8WiUaNGKTIyUq1bt9bcuXPN3UEAAAAAwN9CmQvdFotFL730kg4fPmzMs9lsGjJkiKpVq6aVK1eqa9euGjp0qE6dOiVJOnXqlIYMGaLu3btrxYoVqlKlil588UXZbDZJ0oYNGxQbG6tx48Zp/vz5SkhI0OTJk43tT5o0Sfv379f8+fP1xhtvKDY2Vt98883N3XEAAAAAgMspU6H7yJEj6tmzp44fP243f8eOHTpx4oTGjRunevXq6bnnnlNYWJhWrlwpSVq+fLmaNGmigQMH6s4779TEiROVnJysXbt2SZIWLFig/v37KyoqSk2bNtXYsWO1cuVKZWRk6MqVK1q+fLlGjx6txo0bq0OHDnrmmWe0ePHim77/AAAAAADXUqZC965du9SyZUstW7bMbn5CQoLuuusulS9f3pgXERGh+Ph4oz0yMtJo8/X1VePGjRUfH6+cnBwlJibatYeFhSkrK0tJSUlKSkpSdna2wsPD7badkJCg3Nxck/YUAAAAAPB34OnoAvLr06dPofNTU1NVvXp1u3lVq1bVmTNnrtl+8eJFWSwWu3ZPT09VqlRJZ86ckbu7uypXriwvLy+jvVq1arJYLEpPT1eVKlVu1O4BAAAAAP5mylToLkpGRoZdKJYkLy8vWa3Wa7ZnZmYa04W122y2QtskGdsvrpycnBItj5LJO74cZzg7+jJcCf0ZroT+DFeRd28rm80mNzc3B1dTMs70/iturU4Rur29vZWenm43z2q1ysfHx2j/c0C2Wq2qWLGivL29jek/t/v6+ionJ6fQNknG9osrMTGxRMujdDjOcBX0ZbgS+jNcCf0Z+fn4+CiwTrCsVotxQs9ZWCwWR5dQbFZPi7Kys5V0+BenO87X4hShOyAgQEeOHLGbl5aWZgwZDwgIUFpaWoH2Ro0aqVKlSvL29lZaWprq1asnScrOzlZ6err8/f1ls9l0/vx5ZWdny9Pz6uFITU2Vj4+PKlasWKI6Q0JC5OHhUdrdxDXkXZ/PcYazoy/DldCf4UrozyjKlRyLvLy8S3xSzlFsNpssFou8vb2d5ky3l5e3ynl6qmHDho4updjyPjOuxSlCd2hoqGbNmqXMzEyjo8fFxSkiIsJoj4uLM5bPyMjQwYMHNXToULm7uyskJERxcXFq2bKlJCk+Pl6e+f5BPT09FR8fb9xsLS4uTiEhIXJ3L9l95jw8PPiAvgk4znAV9GW4EvozXAn9GQU4z4hnSTKCtrME7vxc8b1Xpu5eXpQWLVooMDBQ0dHROnz4sGbNmqV9+/apR48ekqTHHntMe/bs0axZs3T48GFFR0erZs2aRsju06eP5syZo40bN2rfvn2KiYlRz5495evrK19fX3Xr1k0xMTHat2+fNm7cqLlz56pfv36O3GUAAAAAgAtwijPdHh4emjlzpkaPHq3u3burVq1amjFjhoKCgiRJNWvW1PTp0zVhwgTNmDFD4eHhmjFjhvHLTufOnZWcnKwxY8bIarWqY8eOGjlypLH96OhoxcTEqH///vLz89OwYcPUsWNHh+wrAAAAAMB1lNnQfejQIbvpWrVqadGiRUUuf9999+m+++4rsn3w4MEaPHhwoW2+vr5655139M4775SuWAAAAAAACuEUw8sBAAAAAHBGhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELohlPJe047AAAAADiDMnv3cpgvM9uqrNxsR5dRIoF1gnUlxyLlOLqS4ivn7ikfTy9HlwEAAADAAQjdf2NZudn6OOlLpWSkO7qUYrNaLfLy8nZ0GcUW4FtJzzXsLB8RugEAAIC/I0L331xKRrqSr6Q5uoxiy8zMZIg5AAAAAKfBNd0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJjEqUL3d999pwYNGtj9N3z4cEnSwYMH9fjjjys0NFSPPfaY9u/fb7fu+vXr1b59e4WGhmrIkCE6d+6c0Waz2TRlyhS1atVKLVq00KRJk5Sbm3tT9w0AAAAA4HqcKnQfOXJEUVFR2rZtm/Hfm2++qStXrmjw4MGKjIzUqlWrFB4erueee05XrlyRJO3bt0+jR4/W0KFDtWzZMl28eFHR0dHGdj/99FOtX79esbGxmjZtmtatW6dPP/3UUbsJAAAAAHARThW6jx49qvr168vf39/4r2LFivrqq6/k7e2tV155RfXq1dPo0aNVoUIFffPNN5KkRYsWqVOnTurWrZsaNmyoSZMmacuWLTpx4oQkacGCBRo+fLgiIyPVqlUrjRgxQosXL3bkrgIAAAAAXIDThe7atWsXmJ+QkKCIiAi5ublJktzc3NSsWTPFx8cb7ZGRkcbygYGBCgoKUkJCglJSUnT69Gk1b97caI+IiFBycrLOnj1r6v4AAAAAAFyb04Rum82mX375Rdu2bdMDDzyg9u3ba8qUKbJarUpNTVX16tXtlq9atarOnDkjSTp79myR7ampqZJk116tWjVJMtYHAAAAAKA0PB1dQHGdOnVKGRkZ8vLy0vvvv6+TJ0/qzTffVGZmpjE/Py8vL1mtVklSZmZmke2ZmZnGdP42Scb6xZWTk1Pi/ULx2Ww24/95oxqcBX0D+eX1B/oFXAH9Ga6E/gxXwffmm6O4tTpN6K5Ro4Z27typW2+9VW5ubmrUqJFyc3M1cuRItWjRokBAtlqt8vHxkSR5e3sX2u7r62sXsL29vY2/JcnX17dENSYmJpZq3xzBx8dHgXWCZbVajB8enIXFYnF0CcVm9bQoKztbSYd/cbrjDPM502cGcC30Z7gS+jPy43vzzeHK35udJnRLUqVKleym69WrJ4vFIn9/f6Wlpdm1paWlGUPGAwICCm339/dXQECAJCk1NVU1a9Y0/pYkf3//EtUXEhIiDw+PEq3jSFdyLPLy8jZ+nCjrbDabLBaLvL29neYXOy8vb5Xz9FTDhg0dXQrKkJycHCUmJjrdZwZQGPozXAn9GUXhe7P5nPF7c95nxrU4Tej+4YcfNGLECP373/82zkD/97//VaVKlRQREaFPPvnEGD5hs9m0Z88ePf/885Kk0NBQxcXFqXv37pKk06dP6/Tp0woNDVVAQICCgoIUFxdnhO64uDgFBQUVuA78Wjw8PJzrA9p5Rm5Ikt2N8pyNU/UL3DRO95kB/AX6M1wJ/RkF8L35pnHF957T3EgtPDxc3t7eev3113Xs2DFt2bJFkyZN0jPPPKMHH3xQFy9e1FtvvaUjR47orbfeUkZGhjp16iRJ6t27t9auXavly5crKSlJr7zyitq2bavg4GCjfcqUKdq5c6d27typqVOnql+/fo7cXQAAAACAC3CaM91+fn6aM2eOJkyYoMcee0wVKlTQE088oWeeeUZubm76+OOP9cYbb+jzzz9XgwYNNGvWLJUvX17S1cA+btw4TZs2TRcuXNC9996r8ePHG9seNGiQfv/9dw0dOlQeHh7q0aOHBgwY4KA9BQAAAAC4CqcJ3ZJ055136tNPPy20rWnTplq9enWR63bv3t0YXv5nHh4eio6OVnR09A2pEwAAAAAAyYmGlwMAAAAA4GwI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAD8fz4+Po4uAYCL8XR0AQAAAHBNmdlWZeVmO7qMEgmsE6wrORYpx9GVFF85d0/5eHo5ugwARSB0AwAAwBRZudn6OOlLpWSkO7qUYrNaLfLy8nZ0GcUW4FtJzzXsLB8RuoGyitANAACuC8Nx8VdSMtKVfCXN0WUUW2ZmJn0awA1F6AYAoAxhOO7NwXBcAMDNQugGAKAMYTiu+RiOCwC4mQjdAACUMQzHBQDAdfDIMAAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAB/Dx8XF0CQAAALgJPB1dAADcCJnZVmXlZju6jGILrBOsKzkWKcfRlRRfOXdP+Xh6OboMAAAAp0LoBuASsnKz9XHSl0rJSHd0KcVitVrk5eXt6DKKLcC3kp5r2Fk+InQDAACUBKH7/7NYLBo7dqy+/fZb+fj4aODAgRo4cKCjywJQAikZ6Uq+kuboMoolMzOTIeYAAAB/A4Tu/2/SpEnav3+/5s+fr1OnTunVV19VUFCQHnzwQUeXBgAAAABwUoRuSVeuXNHy5cv1ySefqHHjxmrcuLEOHz6sxYsXE7oBAAAAAKXG3cslJSUlKTs7W+Hh4ca8iIgIJSQkKDc314GVAQAAAACcGaFbUmpqqipXriwvr/+7QVC1atVksViUnp7uuMIAAAAAAE6N4eWSMjIy7AK3JGPaarVec32bzWYs6+HhceMLNElOTo6CfCrL02l+e7EpyzNL5bzKSXJzdDHFUt3nVuXk5BSrH+H6OFd/pi+jaM7VlyX6M/4K/dl89Oebg75sPmfsyzk5V5/9mpcHi0LoluTt7V3gHzdvujh3F84bgn7w4MEbX5zJ/kd1nKsXOFOtkpQtHT34s6Or+Ntwqv7sLHXmoS/fVE7VlyXnqlWiP99k9GeT0Z9vGvqyyZy4L1/rkmRn+6cwRUBAgM6fP6/s7Gx5el49JKmpqfLx8VHFihWvub6np6dCQkLk7u4uNzfn+CUJAAAAAFB6NptNubm5RoYsCqFbUqNGjeTp6an4+HhFRkZKkuLi4owgfS3u7u4FhqcDAAAAAOAsFyWYytfXV926dVNMTIz27dunjRs3au7cuerXr5+jSwMAAAAAODE327Wu+v6byMjIUExMjL799lv5+flp0KBBGjBggKPLAgAAAAA4MUI3AAAAAAAmYXg5AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdMMpWCwWjRo1SpGRkWrdurXmzp3r6JKA62K1WtWlSxft3LnT0aUApZaSkqLhw4erRYsWatOmjSZOnCiLxeLosoAS++233zRo0CCFh4erbdu2mj17tqNLAm6IwYMH67XXXnN0GX97PKcbTmHSpEnav3+/5s+fr1OnTunVV19VUFCQHnzwQUeXBpSYxWLRyy+/rMOHDzu6FKDUbDabhg8frooVK2rx4sW6cOGCRo0aJXd3d7366quOLg8ottzcXA0ePFghISFavXq1fvvtN7300ksKCAjQww8/7OjygFL78ssvtWXLFj366KOOLuVvjzPdKPOuXLmi5cuXa/To0WrcuLE6dOigZ555RosXL3Z0aUCJHTlyRD179tTx48cdXQpwXY4dO6b4+HhNnDhRd955pyIjIzV8+HCtX7/e0aUBJZKWlqZGjRopJiZGtWvX1n333ae7775bcXFxji4NKLX09HRNmjRJISEhji4FInTDCSQlJSk7O1vh4eHGvIiICCUkJCg3N9eBlQElt2vXLrVs2VLLli1zdCnAdfH399fs2bNVrVo1u/mXLl1yUEVA6VSvXl3vv/++/Pz8ZLPZFBcXp927d6tFixaOLg0otXfeeUddu3bVHXfc4ehSIIaXwwmkpqaqcuXK8vLyMuZVq1ZNFotF6enpqlKligOrA0qmT58+ji4BuCEqVqyoNm3aGNO5ublatGiRWrVq5cCqgOvTrl07nTp1SlFRUXrggQccXQ5QKtu3b9dPP/2kdevWKSYmxtHlQJzphhPIyMiwC9ySjGmr1eqIkgAAfzJ58mQdPHhQ//znPx1dClBq06ZN00cffaT//ve/mjhxoqPLAUrMYrHojTfe0JgxY+Tj4+PocvD/caYbZZ63t3eBcJ03zYcJADje5MmTNX/+fL333nuqX7++o8sBSi3v+leLxaIRI0bolVdeKfDDP1CWxcbGqkmTJnYjkeB4hG6UeQEBATp//ryys7Pl6Xm1y6ampsrHx0cVK1Z0cHUA8Pc2fvx4LV26VJMnT2Y4LpxSWlqa4uPj1b59e2PeHXfcoaysLF26dInL2OBUvvzyS6WlpRn3Qso7UbVhwwbt3bvXkaX9rRG6UeY1atRInp6eio+PV2RkpCQpLi5OISEhcnfnCgkAcJTY2Fh99tlnevfdd3mEI5zWyZMnNXToUG3ZskUBAQGSpP3796tKlSoEbjidhQsXKjs725ieMmWKJGnEiBGOKgkidMMJ+Pr6qlu3boqJidGECRN09uxZzZ07l2utAMCBjh49qpkzZ2rw4MGKiIhQamqq0ebv7+/AyoCSCQkJUePGjTVq1ChFR0crOTlZkydP1vPPP+/o0oASq1Gjht10hQoVJEm1atVyRDn4/wjdcArR0dGKiYlR//795efnp2HDhqljx46OLgsA/rY2bdqknJwcffjhh/rwww/t2g4dOuSgqoCS8/Dw0MyZMzV+/Hj16tVLvr6+6tu3r/r16+fo0gC4CDebzWZzdBEAAAAAALgiLogFAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAEzi6egCAACAlJubq5UrV+qLL77Qzz//rMuXL6tSpUpq2rSpHn/8cUVFRRVYx2Kx6Pz587rttttMr2/VqlWKjo4utK18+fKqVq2a7r77bg0bNkz+/v6lfp0/71Pe67755pt6/PHHS71dAAAcxc1ms9kcXQQAAH9nubm5Gjp0qL7//nvdd999atGihSpWrKiUlBStXbtWx48fV9++ffX6668b6yQmJmrYsGEaPny4unfvbnqNeeG3Q4cO6tChg13b77//rq1bt2r79u2qXbu2Vq1apQoVKpT4NQrbpxMnTmjPnj0KDw/X7bfffkP2BQCAm4kz3QAAONiGDRu0adMmDR8+XEOGDLFrGzx4sPr27auFCxfqoYceUrNmzSRJSUlJOn369E2vtUGDBuratWuB+QMHDtSrr76qNWvWaMWKFerfv3+Jt13YPgUHBys4OLjU9QIA4Ghc0w0AgIP99NNPklToEHIvLy8NHDhQkrR79+6bWldJPfHEE5L+b38AAAChGwAAh/Pz85MkLV26VNnZ2QXaO3TooAMHDui5556TJL322mvGUPPo6Gg1aNDAWPbs2bN666239MADD6hp06Zq2rSpHnroIc2YMcNu29OnT1eDBg30/fff66GHHlKTJk3Uu3fv69qP8uXLFzr/wIEDeumll/Q///M/atKkiZo1a6YnnnhCX331lbFMUfu0atUqNWjQQMuXLzeWbdCggcaMGaNvv/1Wjz32mJo2baqWLVvqlVde0dmzZ+1e22azaf78+ercubOaNm2q+++/X5988olmzJihBg0a6OTJk9e1zwAAXAvDywEAcLDu3btrwYIF+vzzz7V582a1a9dOkZGRioiIUI0aNeTu7i539//7nbxXr17y8PDQihUr1KtXL0VEREiS/vjjD/Xq1UsXL15Unz59dPvttys9PV1r1qzRtGnTlJmZqZdfftnutUeMGKHHHntMTz75pMqVK3dd+7Fp0yZJUpMmTYx58fHxeuqppxQUFKSnnnpKlStX1smTJ/XZZ5/pn//8p2655Ra1adOmyH0qyrZt27Ru3To98cQT6tWrl3bs2KG1a9fqxIkTWrp0qbHcG2+8oWXLlqlly5bq3bu3Tp06pRkzZsjb2/u69hUAgOIidAMA4GC1atXS3LlzFR0drV9++UXLli3TsmXLjLbOnTtr0KBBxhnx8PBwHTlyRCtWrFBYWJhxjfXq1at16tQpTZs2TQ888ICx/V69eumee+7R5s2bC4Tu9u3ba/To0cWuNSMjQ+fOnTOmbTabzp07p++//14zZ85UYGCgnnrqKaN99uzZcnNz08KFCxUQEGDMb9asmQYPHqzNmzerTZs2Re5TUZKTk7V06VLjGveePXsqNTVVu3bt0q+//qratWtr3759WrZsmdq1a6eZM2fKzc1NknT//ffrySefLPY+AwBwPQjdAACUAeHh4frqq68UFxenrVu3avfu3dq/f79+++03zZw5U2vXrtWiRYsUFBRU5Db69eunzp07q3Llynbzz507p1tuuUWXLl0qsM4999xTojrnzJmjOXPmFJhfrlw5tW/fXq+88ordncunTZum8+fPq2rVqsa87Oxs5ebmSlKhNRVHzZo1jcCdp0mTJtq1a5fS0tJUu3Ztff3115Ku3owuL3BLUkREhO655x795z//KdVrAwBQEoRuAADKCHd3dzVv3lzNmzeXJF2+fFlbtmzRzJkzdfjwYU2YMEGxsbHX3MbcuXOVkJCg48eP68SJE7p8+bIk2Z1pzpM/DBdH165d1a1bN0lSenq6Pv/8c+3YsUP9+vXTSy+9JE9P+68W7u7uunDhgubOnatDhw4pOTlZJ06cMK4vzwvfJVXYs8C9vLwkSTk5OZKkX375RZJUp06dAsvWq1eP0A0AuCkI3QAAONCVK1f08ccfKyAgQH369LFrq1Chgh566CG1bt1a999/v7Zv3/6X29q3b58GDhyo7OxstWrVSm3atNGdd96pZs2aqW/fvoUGXA8PjxLVGxwcbHd2vFOnTnrttdc0Z84cnTp1Su+9957dWeWlS5dq7Nixqlatmlq2bKnw8HA1aNBAAQEB6tGjR4leO7/8r1GUrKwsSf8XxvPz8fEp9WsDAFAShG4AABzIx8dH8+bN0y233KLHH3+80JuZVaxYUUFBQfr999//clvvvvuuLl26pC+++EL169c35mdlZen8+fO69dZbb3j9bm5uGj9+vH7++Wd9/fXXatSokXGXdYvFokmTJik4OFirV682rkmXpLi4uBtey5/VqVNH27Zt07Fjx+xu7iZJx44dM/31AQCQeGQYAAAO5e7urscee0ypqal65513Cn1k2O7du/Xzzz/rwQcftFtPsh+eff78efn4+KhWrVp26y9cuFCZmZnGsOsbzcvLS1OmTJG3t7emT5+ugwcPSpIyMzN15coVBQUF2QXu7OxszZ07V5Lsaipsn65Hp06dJEnz58+XzWYz5h8+fFhbt269Ia8BAMC1cKYbAAAHGzFihH7++WctXLhQP/zwgx588EHVrFlTVqtVe/bs0TfffKNGjRrpf//3f411qlWrJkn64osvZLPZ1K1bN91///2aMWOGnn76aXXp0kU2m01bt27Vv//9b/n4+OjSpUuy2WzFGppdUvXq1dNLL72kiRMn6pVXXtGqVat06623qnnz5tqxY4deffVVRUZGKj09XevWrdOxY8fk7u6uixcv/uU+XY+IiAh1795dq1atUlpamu6//36lpqZq0aJFRsA341gAAJAfoRsAAAcrX768FixYoDVr1ujrr7/WypUrlZ6eLh8fH9WrV08jR45Unz597K5Nvueee9SlSxdt2rRJiYmJioyM1IsvvigPDw+tWbNGEydO1K233qo6depoxowZSkxM1EcffaRdu3apZcuWpuxH//799f3332vnzp1677339Oqrr+r999/X1KlTtW3bNn311Vfy9/dXkyZNNGnSJMXExGjPnj26fPmyKlSoUOg+Xa/x48erVq1aWrlypd5++20FBARo2LBhio+P19dff13o9d4AANxIbrb8460AAABcxKVLl+Tu7q7y5csXaBs4cKB27typhISEAndcBwDgRuKabgAA4JK2bt2q8PBwrV692m7+qVOn9NNPP6lJkyYEbgCA6TjTDQAAXNIff/yhTp066cqVK+rdu7dq166ts2fP6vPPP9e5c+c0b948RUREOLpMAICLI3QDAACXlZycrI8//ljbtm1TamqqKlasqIiICL3wwgtq1KiRo8sDAPwNELoBAAAAADAJ13QDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYJL/B7D1+uuuNNi1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the bar plot to show distribution of star ratings given (we hope it matches the train dataset.. it does)\n",
    "plt.figure(figsize=(10, 6))  # Setting an appropriate figure size for clearer visualization\n",
    "pd.Series(stars_preds).value_counts().sort_index().plot(kind='bar', color=\"mediumseagreen\", alpha=0.85)\n",
    "\n",
    "# Adding titles, labels, and other aesthetics for clarity\n",
    "plt.title('Distribution of Predicted Star Ratings', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Star Rating', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.xticks(rotation=0)  # Ensures that the x-axis labels are horizontal\n",
    "plt.tight_layout()\n",
    "\n",
    "# Displaying the enhanced plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7366219429642615\n",
      "0.8182875392706507\n",
      "0.8909968236335549\n"
     ]
    }
   ],
   "source": [
    "cleaned_stars_pred = [s if s in ('0', '1', '2', '3', '4') else -1 for s in stars_preds]\n",
    "# Accuracy\n",
    "print((np.array(cleaned_stars_pred).astype(int) == test_df.head(len(cleaned_stars_pred))['completion'].astype(int)).mean())\n",
    "\n",
    "# < 3 accuracy\n",
    "print(((np.array(cleaned_stars_pred).astype(int) <= 3) == (test_df.head(len(cleaned_stars_pred))['completion'].astype(int) <= 3)).mean())\n",
    "\n",
    "# Accuracy (off-by-1)\n",
    "print(((np.array(cleaned_stars_pred).astype(int) - test_df.head(len(cleaned_stars_pred))['completion'].astype(int)).abs() <= 1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price to train was $82.74736800000001\n",
      "Price for a single inference (on avg): $0.0007341310468123514 and cost to run test: $42.295491999999996\n"
     ]
    }
   ],
   "source": [
    "train_price_usd = 0.008 * job.trained_tokens / 1_000\n",
    "print(f'Price to train was ${train_price_usd}')\n",
    "\n",
    "avg_tokens_per_input = job.trained_tokens / training_df.shape[0]\n",
    "# output is always 1 token\n",
    "\n",
    "avg_inference_price_usd = 0.0120 * (avg_tokens_per_input / 1_000) + (0.0160 / 1_000)\n",
    "avg_inference_price_usd\n",
    "print(f'Price for a single inference (on avg): ${avg_inference_price_usd} and cost to run test: ${avg_inference_price_usd * test_df.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Description</th>\n",
       "      <th>Raw Accuracy</th>\n",
       "      <th>Good vs Bad Accuracy</th>\n",
       "      <th>One-off Accuracy</th>\n",
       "      <th>Cost to Fine-tune (USD)</th>\n",
       "      <th>Cost for a Single Inference (USD)</th>\n",
       "      <th>Cost to Run Evaluation on Test Set (USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Babbage - 1 epoch</td>\n",
       "      <td>0.71472</td>\n",
       "      <td>0.80642</td>\n",
       "      <td>0.87933</td>\n",
       "      <td>1.13342</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>1.60341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Babbage - 4 epochs</td>\n",
       "      <td>0.71979</td>\n",
       "      <td>0.80886</td>\n",
       "      <td>0.88110</td>\n",
       "      <td>4.53000</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>1.60341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5 - 1 epoch + no system prompt</td>\n",
       "      <td>0.73713</td>\n",
       "      <td>0.81980</td>\n",
       "      <td>0.88968</td>\n",
       "      <td>39.88330</td>\n",
       "      <td>0.00036</td>\n",
       "      <td>20.86346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5 - 1 epoch + system prompt</td>\n",
       "      <td>0.73662</td>\n",
       "      <td>0.81829</td>\n",
       "      <td>0.89100</td>\n",
       "      <td>82.75000</td>\n",
       "      <td>0.00073</td>\n",
       "      <td>42.29549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model Description  Raw Accuracy  Good vs Bad Accuracy  \\\n",
       "0                 Babbage - 1 epoch       0.71472               0.80642   \n",
       "1                Babbage - 4 epochs       0.71979               0.80886   \n",
       "2  3.5 - 1 epoch + no system prompt       0.73713               0.81980   \n",
       "3     3.5 - 1 epoch + system prompt       0.73662               0.81829   \n",
       "\n",
       "   One-off Accuracy  Cost to Fine-tune (USD)  \\\n",
       "0           0.87933                  1.13342   \n",
       "1           0.88110                  4.53000   \n",
       "2           0.88968                 39.88330   \n",
       "3           0.89100                 82.75000   \n",
       "\n",
       "   Cost for a Single Inference (USD)  Cost to Run Evaluation on Test Set (USD)  \n",
       "0                            0.00003                                   1.60341  \n",
       "1                            0.00003                                   1.60341  \n",
       "2                            0.00036                                  20.86346  \n",
       "3                            0.00073                                  42.29549  "
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create a dictionary with your data\n",
    "data = {\n",
    "    'Model Description': [\n",
    "        'Babbage - 1 epoch',\n",
    "        'Babbage - 4 epochs',\n",
    "        '3.5 - 1 epoch + no system prompt',\n",
    "        '3.5 - 1 epoch + system prompt'\n",
    "    ],\n",
    "    'Raw Accuracy': [\n",
    "        0.7147171645288389,\n",
    "        0.7197854650860049,\n",
    "        0.7371253015812403,\n",
    "        0.7366219429642615\n",
    "    ],\n",
    "    'Good vs Bad Accuracy': [\n",
    "        0.8064152187874265,\n",
    "        0.8088625830975648,\n",
    "        0.8197976151215871,\n",
    "        0.8182875392706507\n",
    "    ],\n",
    "    'One-off Accuracy': [\n",
    "        0.8793327894745977,\n",
    "        0.881103223230868,\n",
    "        0.8896776769131967,\n",
    "        0.8909968236335549\n",
    "    ],\n",
    "    'Cost to Fine-tune (USD)': [\n",
    "        1.1334228,\n",
    "        4.53,\n",
    "        39.883296,\n",
    "        82.75\n",
    "    ],\n",
    "    'Cost for a Single Inference (USD)': [\n",
    "        2.7830718761390663e-05,\n",
    "        2.7830718761390663e-05,\n",
    "        0.00036213104681235143,\n",
    "        0.0007341310468123514\n",
    "    ],\n",
    "    'Cost to Run Evaluation on Test Set (USD)': [\n",
    "        1.6034112,\n",
    "        1.6034112,\n",
    "        20.863456000000003,\n",
    "        42.295492\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# To make it more readable, let's round the numeric values to a fixed number of decimal places:\n",
    "df_rounded = df.round(5)\n",
    "df_rounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Find the minimum accuracy values for setting xlim\n",
    "min_raw_accuracy = min(df_rounded['Raw Accuracy']) - 0.1\n",
    "max_raw_accuracy = max(df_rounded['Raw Accuracy']) + .02\n",
    "min_good_bad_accuracy = min(df_rounded['Good vs Bad Accuracy']) - 0.1\n",
    "max_good_bad_accuracy = max(df_rounded['Good vs Bad Accuracy']) + .02\n",
    "min_one_off_accuracy = min(df_rounded['One-off Accuracy']) - 0.1\n",
    "max_one_off_accuracy = max(df_rounded['One-off Accuracy']) + .02\n",
    "\n",
    "# Set the style\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# Define figure and axes for the accuracy subplots\n",
    "fig_accuracy, axs_accuracy = plt.subplots(3, 1, figsize=(10, 8))  # Adjust the figure size as needed\n",
    "\n",
    "\n",
    "axs_accuracy[0].barh(df_rounded['Model Description'], df_rounded['Raw Accuracy'], color='skyblue')\n",
    "axs_accuracy[0].set_title('Raw Accuracy')\n",
    "axs_accuracy[0].set_xlim(min_raw_accuracy, max_raw_accuracy)  # Extend the x-axis\n",
    "for i, v in enumerate(df_rounded['Raw Accuracy']):\n",
    "    axs_accuracy[0].text(v, i, \"{:,.2f}%\".format(v * 100), va='center', ha='left', fontweight='bold')\n",
    "\n",
    "# Plot for Good vs Bad Accuracy\n",
    "axs_accuracy[1].barh(df_rounded['Model Description'], df_rounded['Good vs Bad Accuracy'], color='salmon')\n",
    "axs_accuracy[1].set_title('\"Good\" (>= 4) vs \"Bad\" (< 3) Accuracy')\n",
    "axs_accuracy[1].set_xlim(min_good_bad_accuracy, )  # Extend the x-axis\n",
    "for i, v in enumerate(df_rounded['Good vs Bad Accuracy']):\n",
    "    axs_accuracy[1].text(v, i, \"{:,.2f}%\".format(v * 100), va='center', ha='left', fontweight='bold')\n",
    "    \n",
    "# Plot for One-off Accuracy\n",
    "axs_accuracy[2].barh(df_rounded['Model Description'], df_rounded['One-off Accuracy'], color='olive')\n",
    "axs_accuracy[2].set_title('One-off Accuracy')\n",
    "axs_accuracy[2].set_xlim(min_one_off_accuracy, max_one_off_accuracy)  # Extend the x-axis\n",
    "for i, v in enumerate(df_rounded['One-off Accuracy']):\n",
    "    axs_accuracy[2].text(v, i, \"{:,.2f}%\".format(v * 100), va='center', ha='left', fontweight='bold')\n",
    "    \n",
    "# Adjust the layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot for accuracies\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "max_fine_tune = max(df_rounded['Cost to Fine-tune (USD)']) + 7\n",
    "max_eval = max(df_rounded['Cost to Run Evaluation on Test Set (USD)']) + 4\n",
    "\n",
    "# Define figure and axes for the cost subplots\n",
    "fig_cost, axs_cost = plt.subplots(2, 1, figsize=(10, 8))  # Adjust the figure size as needed\n",
    "\n",
    "# Use a lighter green and specify a custom edgecolor for a bolder look\n",
    "light_green_color = '#90ee90'  # Light green color code\n",
    "\n",
    "# Plot for Cost to Fine-tune\n",
    "axs_cost[0].barh(df_rounded['Model Description'], df_rounded['Cost to Fine-tune (USD)'], color=light_green_color, edgecolor='green')\n",
    "axs_cost[0].set_title('Cost to Fine-tune (USD)')\n",
    "axs_cost[0].set_xlim(0, max_fine_tune)  # Extend the x-axis\n",
    "\n",
    "# Annotate with bold text\n",
    "for i, v in enumerate(df_rounded['Cost to Fine-tune (USD)']):\n",
    "    axs_cost[0].text(v, i, \" ${:,.2f}\".format(v), va='center', ha='left', fontweight='bold')\n",
    "\n",
    "# Plot for Cost to Run Evaluation on Test Set\n",
    "axs_cost[1].barh(df_rounded['Model Description'], df_rounded['Cost to Run Evaluation on Test Set (USD)'], color=light_green_color, edgecolor='green')\n",
    "axs_cost[1].set_title('Cost to Run Evaluation on Test Set (USD)')\n",
    "axs_cost[1].set_xlim(0, max_eval)  # Extend the x-axis\n",
    "\n",
    "# Annotate with bold text\n",
    "for i, v in enumerate(df_rounded['Cost to Run Evaluation on Test Set (USD)']):\n",
    "    axs_cost[1].text(v, i, \" ${:,.2f}\".format(v), va='center', ha='left', fontweight='bold')\n",
    "\n",
    "# Adjust the layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot for costs\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using our model in the playground"
   ]
  },
  {
   "attachments": {
    "ad4c9159-de31-41f6-bd9e-14292cc1d585.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screenshot 2023-10-27 at 4.06.12 PM.png](attachment:ad4c9159-de31-41f6-bd9e-14292cc1d585.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3b138a8faad971cc852f62bcf00f59ea0e31721743ea2c5a866ca26adf572e75"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
